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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Kursor JOIV : International Journal on Informatics Visualization MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Multimedia Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Jurnal Minfo Polgan (JMP) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Artificial Intelligence and Digital Business Prosiding Seminar Nasional CORISINDO
Dadang Priyanto
Program Studi Ilmu Komputer, Universitas Bumigora
Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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Comparison of Naive Bayes and Dempster Shafer Methods in Expert System for Early Diagnosis of COVID-19 Nurdin Nurdin; Erni Susanti; Hafizh Al-Kautsar Aidilof; Dadang Priyanto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.2280

Abstract

COVID-19 is a respiratory infection disease caused by the corona virus. Transmission of this virus can spread very quickly so that the number of cases of the corona virus continues to grow and becomes an epidemic that spreads not only in Indonesia but also in other countries in the world. The purpose of this study is to build an expert system that is able to diagnose Covid-19 early by using a comparison of the Nave Bayes method and the Dempster Shafer method. The amount of data used in this study is 550 data, consisting of 500 training data and 50 testing data. While the variables used are symptoms related to COVID-19 as many as 17 symptoms consisting of G01, G02, G03, G04, G05, G06, G07, G08, G09, G10, G11, G12, G13, G14, G15, G16, G17. The diagnostic data consists of Suspected (PDP), Non-Suspected, and Close Contact (ODP). The results of the percentage test by comparing system diagnoses with expert diagnoses, for the nave Bayes method it has an accuracy of 96% with 48 diagnoses according to expert diagnoses from 50 tested data. Meanwhile, the Dempster Shafer method has an accuracy of 40% with 20 diagnoses according to expert diagnoses from 50 tested data. Based on the results of this study, the Naive Bayes and Dempster Shafer methods can be applied to an expert system for early diagnosis of COVID-19, from the results of the system testing the Naive Bayes method has better accuracy than the Dempster Shafer method.
Data Mining Earthquake Prediction with Multivariate Adaptive Regression Splines and Peak Ground Acceleration Dadang Priyanto; Bambang Krismono Triwijoyo; Deny Jollyta; Hairani Hairani; Ni Gusti Ayu Dasriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3061

Abstract

Earthquake research has not yielded promising results because earthquakes have uncertain data parameters, and one of the methods to overcome the problem of uncertain parameters is the nonparametric method, namely Multivariate Adaptive Regression Splines (MARS). Sumbawa Island is part of the territory of Indonesia and is in the position of three active earth plates, so Sumbawa is prone to earthquake hazards. Therefore, this research is important to do. This study aimed to analyze earthquake hazard prediction on the island of Sumbawa by using the nonparametric MARS and Peak Ground Acceleration (PGA) methods to determine the risk of earthquake hazards. The method used in this study was MARS, which has two completed stages: Forward Stepwise and Backward Stepwise. The results of this study were based on testing and parameter analysis obtained a Mathematical model with 11 basis functions (BF) that contribute to the response variable, namely (BF) 1,2,3,4,5,7,9,11, and the basis functions do not contribute 6, 8, and 10. The predictor variables with the greatest influence were 100% Epicenter Distance and 73.8% Magnitude. The conclusion of this study is based on the highest PGA values in the areas most prone to earthquake hazards in Sumbawa, namely Mapin Kebak, Mapin Rea, Pulau Panjang, and Pulau Saringi.
Comparison of Distance Measurements Based on k-Numbers and Its Influence to Clustering Deny Jollyta; Prihandoko Prihandoko; Dadang Priyanto; Alyauma Hajjah; Yulvia Nora Marlim
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3078

Abstract

Heuristic data requires appropriate clustering methods to avoid casting doubt on the information generated by the grouping process. Determining an optimal cluster choice from the results of grouping is still challenging. This study aimed to analyze the four numerical measurement formulas in light of the data patterns from categorical that are now accessible to give users of heuristic data recommendations for how to derive knowledge or information from the best clusters. The method used was clustering with four measurements: Euclidean, Canberra, Manhattan, and Dynamic Time Warping and Elbow approach for optimizing. The Elbow with Sum Square Error (SSE) is employed to calculate the optimal cluster. The number of test clusters ranges from k = 2 to k = 10. Student data from social media was used in testing to help students achieve higher GPAs. 300 completed questionnaires that were circulated and used to collect the data. The result of this study showed that the Manhattan Distance is the best numerical measurement with the largest SSE of 45.359 and optimal clustering at k = 5. The optimal cluster Manhattan generated was made up of students with GPAs above 3.00 and websites/ vlogs used as learning tools by the mathematics and computer department. Each cluster’s ability to create information can be impacted by the proximity of qualities caused by variations in the number of clusters.
Perbandingan Algoritma Sarima dan Prophet Untuk Peramalan Trend Penjualan Voucher Game Online M Rizki; Dadang Priyanto; Galih Hendro Martono; Neny Sulistianingsih; Moch Syahrir
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15083

Abstract

Industri game online terus mengalami perkembangan pesat, mendorong kebutuhan akan sistem peramalan yang akurat untuk mendukung pengambilan keputusan strategis dalam manajemen penjualan dan promosi. Studi ini bertujuan untuk membandingkan kinerja dua algoritma peramalan deret waktu, yaitu Seasonal Autoregressive Integrated Moving Average (SARIMA) dan Prophet, dalam memprediksi tren penjualan voucher game online di platform Kiyystore. Data yang digunakan dalam penelitian ini mencakup transaksi historis dari tahun 2022 hingga 2024, dengan total 5,530 data penjualan. Studi ini menerapkan metodologi Cross Industry Standard Process for Data Mining (CRISP DM) yang terdiri dari tahap pemahaman bisnis, pemrosesan data, pemodelan, dan evaluasi. Model SARIMA dipilih karena kemampuannya untuk menangkap pola musiman dan tren dalam data stasioner. Sementara itu, Prophet digunakan karena dirancang untuk menangani tren non-linear, pola musiman, dan anomali secara otomatis. Evaluasi kinerja dari kedua algoritma dilakukan menggunakan dua metrik utama, yaitu Mean Absolute Error (MAE) dan Root Mean Squared Error (RMSE). Hasil penelitian menunjukkan bahwa Prophet unggul dalam metrik MAE dengan nilai 0,7054, yang menunjukkan kinerja yang lebih baik dalam meminimalkan kesalahan rata-rata. Di sisi lain, SARIMA menunjukkan keunggulan dalam metrik RMSE dengan nilai 0,9514, yang berarti model ini lebih efektif dalam menangani kesalahan besar atau pencilan dalam prediksi. Studi ini memberikan kontribusi penting dalam pemilihan metode peramalan yang sesuai dengan karakteristik data. Dengan memahami keunggulan masing-masing algoritma, pelaku industri game online dapat lebih optimal dalam merencanakan strategi stok dan promosi, sehingga meningkatkan efisiensi dan daya saing bisnis secara keseluruhan
Analisis Pola Pembelian Konsumen Menggunakan Algoritma FP-Growth pada Data Transaksi Restaurant Burger Nindya Alifia Khumaira; Dadang Priyanto; Hairani Hairani; Galih Hendro Martono; Moch. Syahrir; Husain Husain
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 3 (2026): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i3.983

Abstract

Fast-food restaurants generate large volumes of transaction data that can be utilized to understand customer purchasing behavior and support business decision-making. However, transaction data are often used only for operational reporting, limiting their potential for identifying product association patterns. This study aims to apply the Frequent Pattern Growth (FP-Growth) algorithm to discover frequent itemsets and association rules from burger restaurant transaction data and implement the results in a web-based application. The dataset used consists of 2,001 burger restaurant transactions collected from Kaggle, covering the period 2021–2023. The research process included data preprocessing, transaction transformation, FP-Tree construction, frequent itemset extraction, and association rule generation using a minimum support threshold of 2 transactions and a minimum confidence threshold of 60%. The results revealed that the most frequent items were Save Point Sundae (191 transactions), Health Potion Smoothie (181 transactions), and Cheat Code Cookies (164 transactions). Several association rules achieved a confidence value of 100%, indicating a strong co-occurrence relationship between products. Furthermore, the rules Avatar Avocado -> Cosmic Rings and Cosmic Rings -> Avatar Avocado obtained a lift ratio of 1.50, demonstrating a positive association between the two items. These findings indicate that FP-Growth is effective in identifying customer purchasing patterns and can support promotional strategies, product bundling, and inventory management through data-driven decision-making.
Lightweight and Interpretable Coin Recognition and Counting UsingGeometric Detection and Fuzzy Score-Based Classification Ni Gusti Ayu Dasriani; Bambang Krismono Triwijoyo; I Gede Yoga Sudarma Yasa; Dadang Priyanto; Cong Dai Nguyen
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.6067

Abstract

Deep learning-based coin recognition approaches typically require large, annotated datasets and substantial computational resources, yet offer limited interpretability. Such characteristics limit their applicability in lightweight, resource-constrained vision systems. Therefore, this study aims to develop and systematically evaluate a lightweight, interpretable coin recognition and counting method based on geometric detection and fuzzy-score-based classification. The main contribution of this work lies in integrating the Hough Circle Transform, contour-based circularity validation, and a weighted fuzzy score mechanism that aggregates diameter, circularity, and HSV color features without relying on data-driven model training. The proposed approach prioritizes computational efficiency and decision transparency, while maintaining robustness under varying lighting and object configurations. An experimental evaluation was performed on 40 test images containing 362 coins under both bright and dim lighting conditions, with aligned, scattered, and overlapping arrangements. The system achieved a detection rate of 87% and an object-level classification accuracy of 79%. Although image-level accuracy reached 50% under strict evaluation criteria, detailed error analysis indicates that performance degradation is primarily associated with segmentation limitations in overlapping configurations rather than instability in the fuzzy scoring mechanism. These findings demonstrate that a calibrated geometric and fuzzy-based approach can provide a transparent and computationally efficient alternative for small-scale vision applications without requiring large training datasets.
Prediksi Tingkat Realisasi Pupuk Bersubsidi Menggunakan Support Vector Machine (SVM) Muhamad Zaril Akbar; Dadang Priyanto; Ria Rismayati
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.12397

Abstract

Ketidaksesuaian antara alokasi dan realisasi penyaluran pupuk bersubsidi di Provinsi Nusa Tenggara Barat (NTB) menjadi tantangan utama dalam menjaga efektivitas distribusi serta keberlanjutan produksi sektor pertanian daerah. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi model prediksi realisasi pupuk bersubsidi menggunakan algoritma Support Vector Regression (SVR) berbasis kernel Radial Basis Function (RBF). Dataset sekunder yang digunakan mencakup 1.081 data historis alokasi dan realisasi penyaluran lima jenis pupuk bersubsidi (Urea, NPK, SP-36, ZA, dan Organik) periode Januari 2017 hingga Desember 2025 di Kabupaten Lombok Barat. Tahapan metodologi meliputi pembersihan data, seleksi variabel input (tahun, bulan, RDKK, dan alokasi), serta normalisasi data menggunakan metode StandardScaler. Pembagian dataset dilakukan dengan rasio 80% data latih dan 20% data uji. Kinerja model dievaluasi menggunakan metrik Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan koefisien determinasi (R2). Hasil pemodelan menunjukkan bahwa algoritma SVR dengan kernel RBF mampu memodelkan hubungan nonlinier antarvariabel dengan sangat baik. Performa terbaik dicapai pada prediksi pupuk NPK dengan nilai R2sebesar 0,9607, MAE sebesar 0,13, dan RMSE sebesar 0,21, disusul SP-36 (R2 = 0,9558) dan Urea (R2 = 0,6911). Selain itu, model SVR diintegrasikan ke dalam aplikasi berbasis web yang dilengkapi fitur Early Warning System untuk membatasi rekomendasi alokasi agar tidak melampaui kuota. Penelitian ini terbukti efektif memberikan estimasi realisasi distribusi pupuk yang akurat sebagai alat pendukung keputusan bagi pengambil kebijakan pertanian daerah.
Co-Authors Abi Mas'ud Ahmad Deri Dustury Alyauma Hajjah Anthony Anggrawan Apriani Apriani Ayu Dasriani, Ni Gusti Azhar, Raisul Cong Dai Nguyen Deny Jollyta Dwi Oktarina Efendi, Syahril Eldy Waliyamursida Erni Susanti Galih Hendro Martono Guntara, Muhammad Hafizh Al Kautsar Aidilof Hairani Hairani Herman Mawengkang Husain Husain I Gede Yoga Sudarma Yasa I Gusti Ayu Diah Gita Kartika Santi Lalu Arkan Zuhaedi M Rizki Marlim, Yulvia Nora Muhamad Zaril Akbar Muhammad Hairul Abror Muhammad Nur Muhammad Zarlis Musta’an Musta’an Naufal Hanif Neny Sulistianingsih Neny Sulistianingsih Nindya Alifia Khumaira Nurdin Nurdin Prihandoko . Ria Rismayati Siska Rama Dani Solikhun Solikhun Syahrir, Moch. Triwijoyo, Bambang Krismono Willy Fernando