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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) TIN: TERAPAN INFORMATIKA NUSANTARA Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) International Journal of Engineering, Science and Information Technology J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Janitra Informatika dan Sistem Informasi West Science Social and Humanities Studies
Safii, M
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Population Prediction Using Multiple Regression and Geometry Models Based on Demographic Data Safii, M; Setiana, Rika
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4121

Abstract

Population growth is an important issue because it significantly impacts a country’s growth and development. Large population growth can impact potential resources that drive the pace of the economy and national development. On the other hand, it can also be a problem of poverty, hunger, unemployment, education, health, and others. The government needs to control population growth to balance it with good population quality. Data sourced from the Population and Civil Registration Office of Simalungun Regency, Tanah Java sub-district has a high population and continues to increase every year. The impact of the population increase is that it affects the population’s welfare, most of whom work as laborers and farmers. To overcome this problem, it is necessary to predict the number of people in the future so that the government can make the right decisions and policies in controlling the population. This study aims to make predictions using two models, namely Multiple Linear Regression, to find linear equations and Geometry Models for population growth projections. This study utilizes multiple regression analysis and geometric models using three independent variables, namely birth rate (X1), migration rate (X2), and death rate (X3), as well as one bound variable, population number (Y). This study’s results show that the Tanah Java sub-district population is expected to increase in the next five years (2024-2028). Predictions show that by 2024, the population is expected to reach 61178 people from 59589 in 2023. Based on the results of the study, the conclusion of this study it can be used as a guide for the authorities in planning strategies and resource allocation and making a significant contribution in estimating population development in the Java region so that there will be no population explosion in the future so that it does not have a negative impact.
Bibliometric Study on IoT Adoption in Smart Manufacturing: Trends, Challenges, and Opportunities Judijanto, Loso; Ali, Husain; Safii, M; Rasna, Rasna; Timotius, Elkana
West Science Social and Humanities Studies Vol. 3 No. 01 (2025): West Science Social and Humanities Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsshs.v3i01.1640

Abstract

This bibliometric study investigates the adoption of the Internet of Things (IoT) in smart manufacturing, examining the trends, challenges, and opportunities that have shaped this evolving field from 2000 to 2022. Utilizing data sourced exclusively from Scopus and analyzed through VOSviewer, the research identifies core themes, tracks their progression over time, and highlights the key contributors and collaborations across countries. The analysis reveals a significant shift from basic IoT applications towards more sophisticated integrations like machine learning, digital twins, and enhanced security protocols. The study also underscores the crucial role of international collaboration in advancing IoT research and addresses the pressing challenges such as interoperability, security concerns, and the workforce skills gap. Furthermore, it suggests future research directions, including the need for standardized IoT frameworks and the exploration of sustainable IoT practices. This study provides a comprehensive overview of the IoT landscape in manufacturing, offering insights into its future trajectory and the potential for transformative industry advancements.
Support Vector Machine Optimization for Diabetes Prediction Using Grid Search Integrated with SHapley Additive exPlanations Safii, M; Husain, Husain; Marzuki, Khairan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5133

Abstract

The high number of diabetes mellitus sufferers has become a global health issue, and a scientific approach is needed to produce accurate and efficient diagnoses, which can then support decision-making in providing solutions for its management. The goal of this research is to develop a machine learning model that can accurately, efficiently, and transparently diagnose diabetes mellitus for use in clinical practice. This research method involves using the Support Vector Machine (SVM) algorithm, optimized with the Grid Search technique, and evaluated interpretively using the SHapley Additive exPlanations (SHAP) method. This research uses a secondary dataset consisting of the parameters Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, Body Mass Index, DiabetesPedigree- Function, and Age. Data preprocessing was carried out by performing normalization using a standard scaler and dividing the data into training and testing sets. The results of this study show that the SVM model achieved an accuracy of 0.7532 with the optimal parameters C: 1, gamma: 0.01, and kernel: rbf. Using SHAP, the analysis shows that the parameters Glucose, Body Mass Index, and Age have a significant impact on the results of diabetes classification. The main finding of this study is that SupportVector Machine optimization with SHapley Additive exPlanations can deliver excellent performance in diabetes prediction while also enhancing model transparency. The study’s implications suggest that the results can serve as a foundation for developing a medical diagnosis system that is straightforward, accurate, and easy to understand for diabetes mellitus.
Co-Authors Ali, Husain Ananda, Wahyu Aulia, Della DwiSyari, Risti Husain Husain Jaya Tata Hardinata Judijanto, Loso Khairan marzuki M FAUZAN Nababan, Adli Abdillah Qurniawan, Hendry Rasna, Rasna Revi, Ahmad Saragih, Siti Nurmila Setiana, Rika Solikhun Solikhun, Solikhun Suhendro, Dedi Tarigan, Putri Mai Sarah Timotius, Elkana Winanjaya, Riki Zulhamsyah, Azlan