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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Multica Science and Technology
Ilham Sahputra
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Computer Science & IT Energy Engineering Library & Information Science Physics

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Naïve Bayes Classification Algorithm Application on Nutritional Status of Pregnant Women in Lhokseumawe City Ilham Sahputra; Difa Angelina; Mutammimul Ula
Multica Science and Technology Vol 4 No 1 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i1.851

Abstract

The nutritional status of pregnant women is a measure of success in fulfilling nutrition for pregnant women. Poor nutritional status of pregnant women will cause an imbalance of nutrients which can cause nutritional problems in pregnant women. Therefore, we need a system that can predict the nutritional status of pregnant women. This can be implemented by utilizing the naïve Bayes classification algorithm. This research was carried out with the aim of further studying how to apply the Naïve Bayes algorithm to predict the nutritional status of pregnant women, and how the success of this application is based on the accuracy value of the resulting calculations. Based on data on the prevalence and condition of pregnant women in Lhokseumawe and calculations using a series of formulas for mean, standard deviation, probability, and gaussian values, it was found that 50 pregnant women were predicted to have normal nutritional status, while 19 others had nutritional status. not enough. From the results of the accuracy carried out, it was found that the error value (error) of the application used was 48% while the accuracy value of the application was 53% or low. That way, the calculation formula developed in this study needs to be further developed to encourage the accuracy of the application made so that the application results are reliable in real life.
Decision Support System for Land Suitability Assessment of Horticultural Crops of Legume Commodities Using AHP-VIKOR Ilham Sahputra; Rizky Putra Phonna; Natasya Natasya; Annisa Karima; T. Sukma Achriadi Sukiman
Multica Science and Technology Vol 4 No 2 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i2.979

Abstract

This Decision Support System (DSS) is designed to evaluate land suitability for horticultural crops, specifically legumes, using a combination of Analytical Hierarchy Process (AHP) and VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) methods. The system aids farmers in determining the appropriate crops based on the available land conditions. The research includes problem identification, literature review, data collection, and system design. The implementation of the AHP-VIKOR methods has proven effective and accurate in providing horticultural crop recommendations. This system adds value to modern and efficient agricultural land management. The research results show that the AHP-VIKOR methods successfully applied in determining the suitability of land for legumes in the areas of Bireun, Bukit Rata, Sawang, and Pesisir Pelabuhan Kreung Geukuh with satisfactory outcomes. Therefore, the AHP-VIKOR methods are considered optimal for weighting criteria and ranking alternatives in selecting land for legume crops
Comparative Study of VGG16 and MobileNet Architectures for Rice Leaf Disease Classification Using CNN Ilham Sahputra; Ananda Faridatul Ulfa; Bella Amanda Putri; Cut Yuniza Eviyanti
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.15458

Abstract

Rice is a primary commodity in Indonesia's agricultural sector, playing a vital role in national food security. However, rice productivity is frequently disrupted by leaf diseases such as Bacterial Leaf Blight, Brown Spot, Leaf Blast, and Narrow Brown Spot. This study aims to develop an automated rice leaf disease identification model using the Convolutional Neural Network (CNN) method with a transfer learning approach. Two CNN architectures, VGG16 and MobileNet, were trained using a dataset of 2,190 rice leaf images divided into five classes. The research process included data collection, preprocessing, model training, and performance evaluation using a confusion matrix. The results show that the VGG16 model achieved an accuracy of 98%, while MobileNet reached 95% accuracy. Thus, this method can serve as a modern solution for identifying rice plant diseases, supporting early detection efforts and enhancing agricultural productivity.
Pengembangan Aplikasi Cerdas Untuk Pendukung Keputusan Pemilihan Saham Potensial Bagi Investor Pemula Veri Ilhadi; Ilham Sahputra; Sujacka Retno; Siti Fatimatun Zahro; Irvan Na’syakban
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 6 No 2 (2025): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v6i2.13214

Abstract

This study develops an intelligent application to support decision-making in selecting potential stocks for novice investors. Stock selection is often a challenge for beginner investors due to limited experience, analytical skills, and understanding of a company's fundamental indicators. The developed Decision Support System (DSS) integrates two multi-criteria analysis methods: AHP (Analytic Hierarchy Process) and VIKOR (VIšekriterijumsko KOmpromisno Rangiranje). AHP is employed to systematically determine the criteria weights based on user preferences, addressing VIKOR’s limitation in subjective weight assignment. VIKOR is then used to rank stocks by identifying the best compromise solution. The application is user-friendly and utilizes eight fundamental indicators: PER, PBV, ROA, ROE, EPS, BVPS, DR, and DY. Functional testing using the black-box testing method showed that all application features work properly. Recommendation validation also confirmed that the application provides accurate suggestions, consistent with manual analysis using the AHP-VIKOR model.
Co-Authors Ananda Faridatul Ulfa ANNISA KARIMA Bella Amanda Putri Difa Angelina Eviyanti, Cut Yuniza Irvan Na’syakban Mutammimul Ula Natasya Natasya Rizky Putra Phonna Siti Fatimatun Zahro Sujacka Retno T. Sukma Achriadi Sukiman Veri Ilhadi