Hyperemesis gravidarum is a disease that causes excessive nausea and vomiting in pregnant women. Due to dehydration, this disease can interfere with daily work and get worse. Medical personnel generally recognize hyperemesis gravidarum as one type of disease. In fact, hyperemesis gravidarum is divided into 3 levels, namely grade I or general hyperemesis gravidarum, grade II hyperemesis gravidarum and grade III. This shows that information about hyperemesis gravidarum has yet to be widely known by some medical personnel. If this is left untreated, these two conditions can cause deep vein thrombosis in pregnant women. This study aims to apply the Case-Based Reasoning and K-Nearest Neighbor (KNN) methods to produce accurate information on the diagnosis of hyperemesis gravidarum levels in pregnant women based on symptom management in cases of an old diagnosis. The study used medical record data for hyperemesis gravidarum sufferers in 2018-2019, totalling 228 data. The calculation results of the Case-Based Reasoning method with the K-Nearest Neighbor using the confusion matrix produce an accuracy value of 74%, a precision value of 55% and a recall value of 57%, which indicates that this method is good enough to diagnose levels in patients with hyperemesis gravidarum.

One prevalent conversational system within the realm of natural language processing (NLP) is chatbots, designed to facilitate interactions between humans and machines. This study focuses on predicting frequently asked questions by students using the Duel Intent and Entity Transformer (DIET) Classifier method and assessing the performance of this method. The research involves employing 300 epochs with an 80% training data and 20% testing data split. In this study, the DIET Classifier adopts a multi-task transformer architecture to simultaneously handle classification and entity recognition tasks. Notably, it possesses the capability to integrate diverse word embeddings, such as BERT and GloVe, or pre-trained words from language models, and blend them with sparse words and n-gram character-level features in a plug-and-play manner. Throughout the training process of the DIET Classifier model, data loss and accuracy from both training and testing datasets are monitored at each epoch. The evaluation of the text classification model utilizes a confusion matrix. The accuracy results for testing the DIET Classifier method are presented through four case studies, each comprising 25 text messages and 15 corresponding chatbot responses. The obtained accuracy values range from 0.488 to 0.551, F1-Score values range from 0.427 to 0.463, and precision range from 0.417 to 0.457.

One of the Dayak cultures of Kalimantan Island, Indonesia is a traditional house called Lamin where each wall is decorated according to tribal characteristics. This study aims to identify the image on the Lamin wall using the Support Vector Machine (SVM) method based on the eccentricity and metric parameter values. The data of this study consisted of 50 types of images of the Lamin wall motifs of the Dayak Kenyah tribe consisting of tebengaang, dragon, crocodile, tiger, and arch which were taken from the tourist village, Pampang, Samarinda, East Kalimantan. Based on the experiment, the shape feature extraction method has produced the highest value of the eccentricity parameter which is 0.6979 and the metric parameter is 0.9953 on the image of the arch. Motif identification using the SVM method using linear, Gaussian/RBF, and polynomial kernel parameters has resulted in the highest accuracy with 80% image composition of kernel polynomial at 85%, Gaussian/RBF at 80%, and linear at 78%.

Artificial intelligence, especially expert systems, has been widely applied in agriculture to detect pests and diseases that attack a plant. The Mayas rice plant is local in the East Kalimantan region, which has experienced a decline in production due to pest attacks. However, identifying Mayas rice pests is challenging because the symptoms are similar between one type of pest and another. This study used two methods, namely the Bayes Theorem and Dempster Shafer, to determine the highest level of accuracy and the appropriate method for diagnosing Mayas rice pests. The research data consisted of 32 symptoms and ten pests obtained from experts and 50 test data from expert diagnosis and both ways. Comparison testing using the confusion matrix. The results show that the accuracy value for the Bayes Theorem method is 74% and the Dempster Shafer method is 90%, based on the calculation of the confusion matrix. The accuracy value shows that the Dempster Shafer method has the highest accuracy value and is an appropriate method for diagnosing possible types of pests in Mayas rice.

The new coronavirus (COVID-19) has spread to over 200 countries, with over 36 million confirmed cases as of October 10, 2020. As a result, numerous machine learning models capable of forecasting the epidemic worldwide have been produced. This paper reviews and summarizes the most relevant machine learning forecasting models for COVID-19. The dataset is derived from the world health organization (WHO) COVID-19 dashboard, and it contains official daily counts of COVID-19 cases, fatalities, and vaccination use reported by countries, territories, and regions. We propose various convolutional neural network (CNN) based models such as CNN, single exponential smoothing CNN (S-CNN), moving average CNN (MA-CNN), smoothed moving average CNN (SMA-CNN), and moving average smoothed CNN (MAS-CNN). Here, MAPE and MSE are used to assess the suggested models. MAPE is frequently used to compare accuracy across time series with different scales. MSE, the model must strive for a total forecast equal to the entire demand. That is, optimizing MSE seeks to create a forecast that is right on average and so unbiased. The final result shows that SMA-CNN outperformed its baselines in both MAPE and MSE. The main contribution of this novel forecasting approach is a more accurate result as a base of the strategy of preventing COVID-19 spreads.

Crude oil is the world's most crucial commodity since it is used as an essential material in many sectors and as the state budget's price base. The Indonesian Crude Price (ICP) swings in response to changes in global crude oil prices. A pick increase in crude oil prices will undoubtedly cause economic disruption. Thus, the movement or fluctuation of ICP is critical for business actors in the energy industry, particularly in the domestic market. As a result, crude oil price forecasting is required to aid businesspeople in making energy-related decisions. This research uses the Moving Average, and ARIMA approaches to develop an appropriate forecasting model for Indonesian crude oil prices. Within five years or 63 months, the forecasting process employed ICP time-series data per month for 12 different types of crude oil. We discovered that the fittest models for ICP forecasting are ARIMA models (0,1,1), (1,1,0), (0,1,0), and (1,2,1) with MAPE at 16.0967%. The ICP forecasting results from April to September 2020 ICP have a good and proper interpretation, except the type of BRC oil indicates inaccurate forecasts.

Indonesia is a tropical country with a diverse range of plants that ancient people used for traditional medicines. However, the similarity in shape of the leaves became an obstacle to distinguishing them. Therefore, technological advancements are expected to help identify the herbal leaves to use them right on target according to their efficacy. In this research, image classification of katuk (Sauropus Androgynus) and kelor (Moringa Oleifera) leaves is applied using 3 different algorithms i.e hybrid of Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Support Vector Machine (SVM) implementing 4 kernels namely linear, RBF, polynomial, and sigmoid; hybrid of GLCM and Convolutional Neural Network (CNN); and pure CNN. A dataset of 480 images has been collected with 2 different scenarios, including bright and dark intensities. Based on the result, a hybrid of GLCM and SVM showed the highest accuracy of 96% in the dark intensity test using a linear kernel, while sigmoid obtained the lowest accuracy of 35%. On the other hand, it has been discovered that CNN obtained the highest performance in the bright intensity test with an accuracy of 98%. While in the dark intensity test, a hybrid of GLCM and CNN is superior, obtaining 96% accuracy. In conclusion, CNN is more powerful for image classification with bright intensity. For dark intensity images, both the hybrid of GLCM+SVM (linear) and the hybrid of GLCM+CNN are fairly recommended.

In Sabah, agriculture is an important economic sector. The situation has recently worsened due to paddy cultivation and rice production management issues. A traditional form, such as detecting disease with the naked eye, is susceptible to high error rates and incorrect classification. To improve the long-term sustainability of the paddy industry, disease detection is critical. The collaboration between Universiti Malaysia Sabah and other government agency has opened new research opportunities in agricultural programs in Sabah and this has sparked the initiative to use advanced technology in Smart Farming. It allowed researchers to utilize digital technologies to jumpstart sustainable and competitive agriculture development. This paper proposes a Paddy Leaf disease detection and classification algorithm that applies deep learning approach. The proposed solution can later assist farmers to diagnose the frequently occurring disease automatically. CNN architectures have successfully been developed to solve various prediction and classification tasks. Due to its excellent performance, the aim of this paper is to formulate a deep learning approach to recognize and detect paddy disease based on the paddy leaf images. In this work, an optimized deep convolutional neural network model will be utilized and assessed to diagnose the health of the paddy based on its leave condition. Based on the results obtained, the number of epochs and the dropout rate have a great influence on the performance of the CNN models. For instance, having a high number of epoch’s value and smaller percentage of dropout rate, the proposed CNN model is able to classify the type of paddy disease with performance accuracy of 64.80%.

Water quality in fish tanks is essential to reduce fish mortality. Many factors affect the water quality, such as pH, dissolved oxygen, and temperature in fish tanks. Existing work has presented water quality monitoring systems for aquaculture, which are useful for automatic monitoring and notify any incidence of decline in water quality. It enables the fish farms to make interventions to reduce fish mortality. However, advanced monitoring through forecasting is necessary to ensure consistent optimum water quality. This paper presents a web-based water quality monitoring and forecasting system for aquaculture. First, a water quality forecasting model based on the long short-term memory is designed and developed. The model is evaluated and fine-tuned using the existing public dataset. Second, the prototype of the water quality monitoring and forecasting system is developed. An Arduino and Raspberry Pi based water quality data acquisition tool is built. A web-based application is then developed to present the monitoring data and forecasting. A notification module is included to send an alert message to the fish farmers when necessary. The system is tested and evaluated at the fish hatchery in Universiti Malaysia Sabah. The findings show that the proposed system provides better water quality management for fish farms.

Pengelolaan sistem inventaris pada Laboratorium Komputasi dan Pemrograman Komputer (KPK), Fakultas Teknik, Universitas Mulawarman memiliki beberapa masalah antara lain banyaknya file Microsoft Excel yang menyulitkan pencarian, kodefikasi barang yang masih belum sesuai dengan Peraturan Menteri Keuangan (PERMENKEU) Nomor 29/PMK.06/2010. Berdasarkan dari masalah yang ada pengembangan Sistem Informasi Manajemen Inventaris (SIM-VENTAR) dengan menggunakan data flow diagram (DFD) dan relasi database telah dirancang dan dibangun. Setelah itu, dilakukan uji blackbox untuk mengetahui sistem sudah berjalan sesuai dengan fungsinya. Hasil dari penelitian ini adalah terbangunnya sebuah SIM-VENTAR yang terdiri dari halaman menu utama, data inventaris, data aset, data SIMAK-BMN, data perawatan, list data inventaris, list nilai aset, list data SIMAK-BMN, list data perawatan, dan list laporan. Tahap akhir yaitu tahap dimana dilakukan pengisian data inventaris keseluruhan pada database dimana sistem ini dapat membantu pihak Laboratorium secara baik, cukup efektif dan efisien.

Wilayah potensial untuk menanam lada semakin berkurang, sehingga jumlah produksi lada menjadi semakin menurun. Hal ini tentunya perlu menjadi perhatian mengingat lada merupakan salah satu komoditas unggulan yang sangat penting untuk menunjang perekonomian. Informasi tentang daerah yang berpotensi sebagai daerah penghasil tanaman lada perlu dilakukan. Penelitian ini bertujuan untuk mendata dan menganalisa wilayah potensial untuk tanaman lada menggunakan pendekatan algoritma cerdas yaitu K-Means. Data penelitian berasal dari Dinas Perkebunan Provinsi Kalimantan Timur sebanyak 1200 data dalam rentang waktu tahun 1990 sampai 2019 telah digunakan untuk dianalisis. Lebih lanjut, ketiga metode jarak yaitu Euclidean Distance, Manhattan Distance dan Minkowski Distance digunakan dalam penelitian ini. Dari ketiga metode tersebut dicari nilai akurasi yang tertinggi menggunakan metode Silhouette Coefficient (SC). Metode Sum Square Error (SSE) dan R-squared (R2) juga digunakan untuk mengukur cluster optimal. Hasil percobaan memperlihatkan bahwa metode jarak Manhattan Distance memiliki nilai akurasi terbaik. Sedangkan, cluster optimal untuk klusterisasi wilayah diperoleh tiga cluster yang merupakan cluster ideal untuk mengelompokkan wilayah penanam lada dengan SSE sebesar 238.7377116 dan nilai R2 adalah 0.459398609. Berdasarkan hasil tersebut, diperoleh informasi tentang wilayah yang berpotensi untuk produksi lada menggunakan tiga kategori yaitu kurang berpotensi, cukup berpotensi dan berpotensi baik dengan algoritma K-Means dan metode jarak Manhattan Distance.

Mandau is a sharp weapon like a machete that comes from the culture of the Dayak tribe in Kalimantan. Mandau itself has many types of carvings with different motifs in each typical Dayak tribe. The diversity of Dayak clumps and sub-tribes produces various types of Mandau, which are similar. Most Indonesians cannot distinguish between the existing variety of Mandau because of the information lack about Mandau. The study aims to identify Mandau types by applying Support Vector Machine (SVM) methods according to shape and texture feature extraction of image Mandau. Process of feature extraction using eccentricity, metric, contrast, correlation, energy, and homogeneity parameters. Six parameters obtained from the image feature extraction were used for the classification process. There are four kind of Mandau that used, such as Mandau Benuaq, Mandau Kenyah, Mandau Mahah, and Mandau Tunjung, with each Mandau 24 images, and the total data is 96. The implementation of the SVM method in identifying the type of Mandau using Linear Kernel, Gaussian / RBF Kernel, and Polynomial Kernel. The results indicated that when SVMs were used to identify Mandau images in split data, 80% of the training and 20% of the test data had the highest average accuracy of 82%. Based on the type of Kernel at 80:20 data split, the identification of the Mandau image using the Polynomial Kernel has the highest accuracy rate of 95%.

This study aims to explore the perceptions and experiences of Teacher-Educators (TEs) who participated in virtual research-workshop-series as professional development programs. Six TEs, three from natural science and three from social science, participated in a nine-month virtual research workshop series organized by the faculty. In the frame of a case study, the data were gathered from in-depth interviews and a set of questions. The findings revealed that TEs had sufficient research knowledge as they were able to identify good quality of research, read relevant reading research, and signified the importance of research as part of their professional identity. Completion of other tasks, lack of research motivation and collegiality, shortage of research skills and competencies including how to read academic articles due to vocabulary and sentence construction hindered them from conducting research. The workshop has facilitated the TEs autonomy, research skills and competencies, research collaboration, and goal-orientation. The PD program strengthened their research motivation and engagement that scaffold positive insights into their self-research awareness. Moreover, all TEs were able to complete their papers and submit them to reputable journals.

Persediaan obat pada suatu Puskesmas seringkali habis sebelum jadwal penerimaan obat dilakukan hal ini dikarenakan Kejadian Luar Biasa (KLB). Sehingga, perencanaan persediaan obat yang efektif dan efisien dengan menerapkan metode kecerdasan buatan dalam rangka membantu pihak manajemen sangat diperlukan. Penelitian ini bertujuan untuk memonitoring persediaan obat sebagai salah satu dasar dalam permintaan obat. Data pemakaian obat yang digunakan berasal dari Laporan Pemakaian dan Lembar Permintaan Obat (LPLPO) UPTD Puskesmas Lempake tahun 2016-2018 dan telah dinormalisasi dengan metode Z-Score. Metode K-Means telah diterapkan sebanyak 3 cluster terdiri tinggi (C1), sedang (C2) dan kurang (C3) dimana penentuan titik centroid berdasarkan nilai max, average, dan min. Sedangkan, metode jarak Euclidean distance telah ditetapkan untuk menganalisis jarak data tiap cluster. Hasil temuan mengindikasikan bahwa pengujian cluster menggunakan Sum of Squared Error (SSE) telah mendapatkan nilai sebesar 77,34814. Dimana, hasil pengelompokkan yaitu C1 sebanyak 5 data, C2 sebanyak 14 data, dan C3 sebanyak 206 data. Hal ini berarti bahwa 3 cluster merupakan hasil terbaik pengelompokkan. Metode K-Means dapat menjadi alternatif dalam membuat model analisis monitoring persediaan obat di Puskemas.

This study aims to assess the performance of non-ASN employees at the Human Resources Development Agency (BPSDM), East Kalimantan Province, Indonesia in order to assist organizers in determining the feasibility of extending work contracts. The performance of 37 non-ASN employees has been assessed based on 12 criteria including honesty, discipline, loyalty, responsibility, courtesy, commitment, ability and skills, neatness, communication, achievement, absence, and violations. In this study, the Rank Order Centroid (ROC) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) methods have been implemented to obtain rankings. Meanwhile, the confusion matrix (CM) method has also been used to measure the accuracy of both methods. Based on the experiment, the ROC method has been used to achieve the criteria weight and the MOORA method has been utilized to rank all non-ASN employees based on the highest score. Where the CM suitability level of 81.1% has been gained so that the ranking of 37 non-ASN employees can be revealed. The study indicates that both methods can be implemented as alternative models in assessing the performance of non-ASN employees. Therefore, these methods are quite effective, efficient, and relatively easy to use.

Sistem Informasi berbasis website salah satu teknologi yang terdiri dari teks, gambar, file, dan suara animasi dan dengan kemampuan itu media informasi akan lebih menarik dan dimininati untuk dipergunakan sebagai media penyebaran informasi. Sistem Informasi berbasis website mampu mengolah data menjadi sebuah informasi dengan cara mengidentifikasi, mengumpulkan, mengelola dan menyediakan untuk diakses secara bersama-sama. Sistem informasi website Koordinator Kepegawaian Universitas Mulawarman merupakan sistem yang dipergunakan untuk memberikan kemudahan dalam mendapatkan informasi yang dibutuhkan pengguna dan selain itu, informasi yang di manajemenn oleh website akan tersimpan dengan baik dan tidak mudah hilang pada sistem sehingga memudahkan pegawai dalam mencari informasi. Sistem Informasi Koordinator Kepegawaian menggunakan sistem Content Management System (CMS) dalam implementasinya karena kemampuan dari CMS yang cocok dengan permasalahan sistem yang ada pada Koordinator Kepegawaian dan penelitian ini menggunakan metode waterfall dalam perencangan sistem Koordinator Kepegawaian. Hasil pada penilitan ini yaitu, informasi lebih mudah dicari dan tidak mudah hilang karena sistem CMS ini mampu membuat kategori-kategori berdasarkan kebutuhan Koordinator Kepegawaian.

Diabetes Mellitus (DM) is a chronic disease that occurs when the body cannot effectively use the insulin it produces. The use of artificial intelligence (AI) can provide a means to diagnose. This study aims to obtain the best classification of the Naïve Bayes (NB) and K-Nearest Neighbors (KNN) methods so that accurate results are obtained in diagnosing DM disease using a dataset originating from The Abdul Moeis Hospital, Samarinda, East Kalimantan, Indonesia. The results showed that the KNN performed better in accuracy, precision, and specificity with an Area Under the Curve (AUC) value 10% higher than NB. Overall, KNN obtained a better recall compared to the NB in order to DM diagnosis.

Herbal plants have been used for generations by the community as alternative medicine because herbal plants have properties to treat various diseases. The development of traditional medicine that is increasingly globalized makes the number of herbal plant productions increase. The availability of herbal plants can be done by cultivating herbal plants in the correct location. However, in the cultivation of herbal plants, harvest failures are often found due to climatic factors and soil conditions that are not following the requirements for growing these herbal plants. Therefore, recommendations about the right location for growing herbal plants are needed. This study uses Fuzzy Multiple Attribute Decision Making (FMADM) with the Weighted Product (WP) method, which is applied to a decision support system to find suitable herbal plants planted in a field. The criteria used are based on plant growth consisting of rainfall, annual average temperature, altitude, and soil acidity. The result of this study is a decision support system that can provide recommendations for herbal plants that are suitable for planting in the three locations in East Kalimantan Province based on the highest preference value.

Insurance product offerings are not always understood by prospective customers (CN) due to limited information related to products. This can cause confusion so that CN does not want to buy it. The purpose of this study is to analyze the selection of insurance products PT. AIA Financial Samarinda, East Kalimantan, Indonesia uses the Analytical Hierarchy Process (AHP) and Multi Objective Optimization on the Basis of Ratio Analysis (MOORA) approach so that CNs can choose based on insurance product facilities that match their abilities. In this study, as many as 10 types of insurance products and 10 CN criteria were then analyzed based on the two methods used. Then, the calculation accuracy of the two methods has been using the confusion matrix (CM) method. Based on the results of CM calculations from 27 CN datasets with a conformity level of 81.5%, it has been obtained which indicates that the two methods can be implemented as an alternative in choosing insurance products according to ability or based on CN criteria. The results show that this method is quite effective, efficient and relatively easy to use in determining insurance products that meet the criteria or according to CN's economic capabilities

Continuous development is the key of development issue in developing nations. Smart city measurement is prevalently carried through in the cities in which the nations have been classified as industrialized countries. In addition, cities in Europe becomes the models of smart city system. Smart city concept used in the cities in Europe applies six predominant features i.e. smart economic, smart mobility, smart environment, smart people, smart living, and smart governance. This paper focuses on figuring out city??? development strategy in developing nations particularly Indonesia in regard with European Framework by way of Multi Expert Multi Criterion Decision Making (ME-MCDM). Recommendation is resulted from the tests using the data collected from one of the metropolis cities in Indonesia, whereby issuing recommendation must firstly implement smart education, secondly communication, thirdly smart government, and fourthly smart health, as well as simultaneously implement smart energy and smart mobility.