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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Bulletin of Informatics and Data Science Bulletin of Artificial Intelligence
Mustakim Mustakim
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Prediksi Status Penanganan Pasien Covid-19 dengan Algoritma Naïve Bayes Classifier di Provinsi Riau Dedi Pramana; Mustakim Mustakim
Jurnal Sistem Komputer dan Informatika (JSON) Vol 3, No 2 (2021): Desember 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i2.3570

Abstract

Covid-19 is a new virus that emerged at the end of 2019 in Wuhan city, China.  This virus continues to grow until it spreads to various countries in the world.  As a result, there has been a large accumulation of Covid-19 patients in every hospital in every country affected by Covid-19.  Covid-19 patients receiving treatment in hospitals have different conditions and severity, this of course affects the different mechanism for handling patients.  Therefore, technological support is needed to help classify the treatment of patients so that they can be concentrated on patients who can be treated with isoman treatment or must be referred to hospital.  This research was conducted to build a model based on a dataset of patients infected with Covid-19 using the Naive Bayes Classifier algorithm.  The model built can predict the treatment status of patients based on age and gender who have the highest probability of being treated in an isoman way or having to be referred to hosspital. Data used is applied using Rapidminer with validation used is spill validation with the ratio of training data is 70% and test data is 30%.  The results of this research indicate classification using the Naive Bayes Classifier algorithm has a high level of accuracy in classifying patient status data, rately 83.33%.
Penerapan Algoritma Support Vector Regression untuk Prediksi Jumlah Pasien Covid-19 di Provinsi Riau Adyah Widiarni; Mustakim Mustakim
Building of Informatics, Technology and Science (BITS) Vol 3 No 2 (2021): September 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (396.346 KB) | DOI: 10.47065/bits.v3i2.1004

Abstract

In 2019, at the end of December, there was an outbreak of a disease with an unknown cause in Wuhan, Hubei Province, China. The World Health Organization has named the outbreak of the disease as coronavirus caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or Covid-19. Covid-19 is a disease outbreak that has spread in various regions of Indonesia, such as in Riau, at PT. Nusantara V Plantation (PTPN V). So we need a way to increase awareness and vigilance, namely by presenting information using Data Mining in predicting the number of cases with thealgorithm Support Vector Regression (SVR). The prediction process is carried out using SVR by specifying the SVR and Kernel Linear parameters. The SVR algorithm can predict the number of Covid-19 patients in the next 30 days so that the Correlation Coefficienti (R) level is 85% and the Mean Square Error (MSE) value is 0.196. From the results of the experiment, there was a decrease in cases of Covid-19 patients at PT. Perkebunan Nusantara V in the next 30 days, with the acquisition of the best minimum sensitivity value of 0.09
Penerapan K-Means dan Fuzzy C-Means untuk Pengelompokan Data Kasus Covid-19 di Kabupaten Indragiri Hilir Sania Fitri Octavia; Mustakim Mustakim
Building of Informatics, Technology and Science (BITS) Vol 3 No 2 (2021): September 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.275 KB) | DOI: 10.47065/bits.v3i2.1005

Abstract

In the beginning of 2020 world was shocked because new virus spreaded, that is Coronavirus Disease 2019 (Covid-19). This virus spread quickly in almost country, including Indonesia. Covid-19 virus deployment started in various regions in Indonesia stay increasing everyday. this research has been done the region clustering that infected Covid-19 case in Indragiri Hilir district to inform to central government about Covid-19 handling. To do Clustering in this research used K-Means and Fuzzy C-Means Algorithm. After done some of test, it's obtained the ratio which was tested with Silhouette Index and Partition Coefficient, SI validity value of K-Means is 0,950 while PCI validity value of Fuzzy C-Means is 0,960. The results have been obtained shown that Fuzzy C-Means Method is the best Method to do Clustering Covid-19 data in Indragiri Hilir district Because the validity value is closed to 1 which is located in K=3.
Perbandingan Algoritma K-Means Dan K-Medoids Pada Pengelompokan Humidity, Temperature, Dan Voltage Di Data Center Perawang Nanda Try Luchia; Mustakim Mustakim
Journal of Information System Research (JOSH) Vol 4 No 1 (2022): October 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.938 KB) | DOI: 10.47065/josh.v4i1.2385

Abstract

A data center is a facility managed by a company for data storage (database) and telecommunications from all computer system components and needs. Monitoring the level of humidity, temperature, and voltage is needed to support the performance of the data center and can be done with AKCP. Because of that, it is necessary to group Humidity, Temperature and Voltage of Perawang DC to optimize the monitoring process. Various methods are used to make it easier for companies to determine the best grouping of performance data found in the data center. In this research, data was obtained from Systemlog AKCP PT. Arara Abadi, Perawang from January 21, 2022 – March 19, 2022. This research is expected to make it easier for companies to determine which algorithm is the right one for grouping air humidity, temperature and voltage levels in the Perawang data center by comparing two algorithms namely K-Means and K-Medoids. Based on the research results, K-Means is better in grouping the Humidity, Temperature and Voltage data of Perawang DC in PT. Arara Abadi, Perawang because it has accurate cluster accuracy compared to K-Medoids with a DBI value of 0.306 in the K=2 experiment and the process time is only 1 minute 22 seconds.
Implementasi Algoritma Naïve Bayes Classifier (NBC) untuk Klasifikasi Penyakit Ginjal Kronik Qurotul A'yuniyah; Ena Tasia; Nanda Nazira; Pangeran Fadillah Pratama; Muhammad Ridho Anugrah; Jeni Adhiva; Mustakim Mustakim
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 1 (2022): September 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4781

Abstract

Degenerative disease is a non-communicable disease that arises from an unhealthy lifestyle, so that it can reduce the physical and mental quality of the sufferer. Chronic Kidney Disease (CDK) is a degenerative disease that is included in the world's top 10 causes of death according to the World Health Organization (WHO). This study used CDK data with attributes of age, blood pressure, weight, albumin levels, sugar levels, red blood cells, pus cells, pus cell clots, bacteria, blood sugar levels, blood urea levels, creatinine serum, sodium, magnesium, hemoglobin, the volume occupied by red blood, indications of hypertension, indications of diabetes mellitus, indications of coronary heart disease, appetite, indications of swelling in the calves or feet, and indications of anemia. Therefore, the classification of kidney disease data is carried out with the implementation of the superior Naïve Bayes Classifier (NBC) algorithm and produces a high level of accuracy. The classification results using the RapidMiner tools carried out by the application of the NBC algorithm, the accuracy value is 96.43%, the average recall is 93.18%, the average precision is 93.02%, and the AUC is 93.2%. so it can be concluded that the performance of NBC in classifying chronic kidney disease data is excellent.
Seleksi Fitur untuk Prediksi Hasil Produksi Agrikultur pada Algoritma K-Nearest Neighbor (KNN) Delvi Nur Aini; Bella Oktavianti; Muhammad Jalal Husain; Dian Ayu Sabillah; Said Thaufik Rizaldi; Mustakim Mustakim
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 1 (2022): September 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4813

Abstract

Agriculture is one of the largest economic driving sectors in Indonesia. The Central Statistics Agency (BPS) in 2021 recorded that 37.02% of Indonesia's population worked in the agricultural sector. The problem faced by farmers today is the decline in yields, both in quantity and quality due to unpredictable weather, making it difficult for farmers to choose the types of plants that are suitable for planting. The application of data mining techniques has problems related to the complexity of weather parameters and natural conditions that support agricultural production, so it is very important to do feature selection, namely to form the most relevant features. This study conducted an experiment to determine the effect of implementing the Principal Component Analysis (PCA) selection feature on the performance of the K-Nearest Neighbor (KNN) algorithm which produces the highest accuracy of 99.64% in this study.
Implementasi Deep Learning Menggunakan Metode You Only Look Once untuk Mendeteksi Rokok Ahmad Harun; Mustakim Mustakim; Oktaf Brilian Kharisma
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5409

Abstract

Cigarettes are processed products from tobacco products which are used by burning and then smoked. Smoking activities are often found in everyday life, including in public infrastructure. The approach taken to prevent this activity generally uses manual information or human intervention. In terms of this approach, there are often many problems and failures due to the lack of manpower and supporting rules. Therefore, this study was structured with the aim of being able to detect smoking objects in real time using the You Only Look Once (YOLO) method. YOLO which is based on deep learning is very good at detecting objects, this model provides a single convolution neural network in assigning location and classification. So that in its application, YOLO is very fast in detecting and recognizing objects. This study conducted experiments on the training dataset in testing the YOLOv3, YOLOv3-Tiny and YOLOv4 models. The best training results were obtained in the YOLOv4 model with a composition of 80% training and 20% validation data sharing with a Mean Average Precision (mAP) of 92.54% and an F1-Score of 0.89. This study also conducted experiments on testing to detect cigarettes in real time, where the system can detect cigarettes up to a distance of 4.5 meters, and the highest detection accuracy is obtained at a distance of 1 meter, namely 99.03%.
Penerapan Algoritma Support Vector Regression dalam Memprediksi Produksi dan Produktivitas Kelapa Sawit Adyah Widiarni; Mustakim Mustakim
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.6089

Abstract

Palm oil is a plantation crop that provides the highest economic value in Indonesia. Riau is currently the highest palm oil producing province in Indonesia with a state-run palm oil company, PTPN V. However, palm oil production is not always stable every month, whichexperiences ups and downs in the amount of production and productivity due to several factors including irregular rainfall, climate, soil fertility and most importantly fruit bunches that are not ready to harvest. So the data mining processing process is carried out by predicting the amount of production and productivity of oil palm applying the Support Vector Regression (SVR) algorithm with three kernels such as the Linear kernel, RBF kernel and Polynomial kernel. Experimental results on palm oil production and productivity show that the best kernel is the RBF kernel because the prediction results are close to the actual value. The accurate rate on palm oil production is 75.4% and palm oil productivity produces an accuracy value of 71%. It also produces an error value on palm oil production of 1.8%, for productivity of 2.1%. The results of the study can be used as an estimated picture in the company's future decision making.
Analisis Sentimen Pengguna Transportasi Online Maxim Pada Instagram Menggunakan Naïve Bayes Classifier dan K-Nearest Neighbor Dzul Asfi Warraihan; Inggih Permana; Mustakim Mustakim; Rice Novita
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6336

Abstract

Online transportation is a form of internet-based transportation that covers all aspects of the transaction process, including booking, route tracking, payment, and service assessment of the online transportation. Maxim is one of the popular online transportation providers in Indonesia so it will continue to improve its services to serve the needs of the entire community. In making developments, Maxim needs user opinions regarding its application or services. This research conducts sentiment analysis of Maxim users' opinions on Instagram using Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. Opinions are divided into 3 classes: negative, neutral, and positive. This research also uses the Random Over Sampling method and data sharing with 10-Fold Cross Validation. The accuracy results on sentiment data related to applications using the NBC algorithm are 81.03% and in the KNN algorithm with a value of k = 3 which is 80.72%. Meanwhile, sentiment data related to services produces an accuracy value in the NBC algorithm, namely 94% and the KNN algorithm with k = 3, namely 84%. It can be concluded that the NBC model is better than the KNN model in testing application-related sentiment data and service-related sentiment data after the Random Over Sampling method.
Analisis Sentimen Terhadap Pemindahan Ibu Kota Negara Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neightbors Dedi Pramana; M Afdal; Mustakim Mustakim; Inggih Permana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6523

Abstract

The relocation of Indonesia's capital city is a hot topic of discussion at the moment. So that this government policy reaps a lot of reactions from various parties, especially the general public in Indonesia. Various reactions were shown with various expressions on various social media. One of the social media that has become a place for people to express themselves in responding to this government policy is Instagram. The comments poured by the community on posts on Instagram are very diverse ranging from positive, negative, and neutral comments. If these comments are processed properly, they can be used as evaluation material for the relocation of the State capital. Seeing this, a sentiment analysis is needed which is intended to classify the various comments so that they can be presented into information which will be intended to help the government make considerations in carrying out policies towards moving the national capital. In this study, data processing was carried out with the Naive Bayes Classifier and K-Nearest Neightbors algorithms with Instagram comment data on posts related to moving the national capital. Where the amount of data used is 2,404 comments. It was found that the accuracy of the NBC algorithm was 63.09% and K-Nearest Neightbors was 69.23% so it can be concluded that KNN is better than NBC. In addition, the popularity of public sentiment towards the relocation of the National Capital was also obtained with a positive sentiment of 28% totaling 643 comments, a neutral sentiment of 42% totaling 1025 comments, and a negative sentiment of 30% totaling 730 comments.
Co-Authors Adyah Widiarni Ahmad Harun Bella Oktavianti Dedi Pramana Delvi Nur Aini Dian Ayu Sabillah Dzul Asfi Warraihan Ena Tasia Inggih Permana Jeni Adhiva Kharisma, Oktaf Brillian M Afdal Muhammad Jalal Husain Muhammad Ridho Anugrah Muhammad Ridwan Nanda Nazira Nanda Try Luchia Pangeran Fadillah Pratama Qurotul A'yuniyah Rice Novita Said Thaufik Rizaldi Sania Fitri Octavia Siti Monalisa Suryani Suryani