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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Bulletin of Electrical Engineering and Informatics Jurnal Sarjana Teknik Informatika Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Bulletin of Electrical Engineering and Informatics Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Infomedia Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Mnemonic Journal of Digital Education, Communication, and Arts (DECA) Jurnal of Applied Multimedia and Networking International Journal of Advances in Data and Information Systems Computer Science and Information Technologies TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Walisongo Journal of Information Technology SinarFe7 Didaktika Religia Jurnal Informatika dan Teknologi Pendidikan Innovative: Journal Of Social Science Research Bulletin of Social Informatics Theory and Application Aksa : Jurnal Desain Komunikasi Visual Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Sains Komputer dan Sistem Informasi Jurnal Riset Multidisiplin dan Inovasi Teknologi Jurnal ilmiah teknologi informasi Asia
Fresy Nugroho
UIN Maulana Malik Ibrahim
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Comparative Analysis of Artificial Neural Networks, Linear Regression, Random Forest, and Support Vector Machine for Predicting Poverty Levels in Indonesia Alfia, Lia Alfia; Nugroho, Fresy; Arif, Yunifa Miftachul
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1467

Abstract

Poverty remains a persistent and complex challenge in Indonesia, driven by multiple interrelated socioeconomic factors. Accurate poverty prediction is essential to support effective policy formulation and targeted interventions. This study evaluates and compares the performance of four machine learning models for predicting poverty levels in Indonesia: Artificial Neural Networks (ANN), Linear Regression, Random Forest, and Support Vector Machine (SVM). A quantitative approach is employed using provincial-level data from 2015 to 2023, consisting of 306 observations and 13 socioeconomic indicators related to education, employment, health, infrastructure, and economic conditions. Data preprocessing includes data cleaning, Min–Max normalization, and feature selection. Model performance is assessed using Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results show that ANN achieves the best predictive performance, with the lowest MSE (0.0132) and MAE (0.0815), and the highest R² value (0.924). Random Forest and SVM demonstrate competitive performance, while Linear Regression yields the weakest accuracy. These findings confirm the effectiveness of ANN for poverty prediction and support its use in data-driven poverty reduction policies in Indonesia.
Car selection in games using multi-objective optimization by ratio analysis based on player achievement Putra, Caesar Nafiansyah; Nugroho, Fresy; Imamudin, Mochamad; Pebrianti, Dwi; Hammad, Jehad Abdelhamid; Lestari, Tri Mukti; Maharani, Dian; Nurrahman, Alfina
Computer Science and Information Technologies Vol 7, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i1.p30-45

Abstract

The selection menu in some racing games usually uses a random system for vehicle selection. However, this random feature generally randomizes the selection of the index without considering factors that support the player's abilities. Therefore, this study aims to develop a racing game that can suggest vehicles that have been adjusted to the player's performance. Vehicle recommendations are made using the multi-objective optimization on the basis of ratio analysis (MOORA) method as its method. The MOORA calculation ranks vehicles based on criteria such as mileage, fuel efficiency, speed, agility, and others collected in previous games. The results of this study show the effectiveness of using the MOORA method in recommending vehicles that match the player's skills, thereby improving the overall player experience. In addition, the usability test produced a system usability scale (SUS) score of 82.4, so it is included in the very good category.
Serious game intelligent transportation system based on internet of things Nugroho, Fresy; Buditjahjanto, I Gusti Putu Asto; Pebrianti, Dwi; Hammad, Jehad A. H.; Fachri, Moch; Lestari, Tri Mukti; Maharani, Dian; Nurrahma’N, Alfina
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp177-190

Abstract

This research examines the implementation of the preference ranking organization method for enrichment evaluation (PROMETHEE) approach for multi-criteria decision-making in a character recommendation system for serious games. The method calculates character skill values across multiple criteria and generates rankings of the best characters according to game environment conditions derived from closed-circuit television (CCTV) based traffic detection. Image processing algorithms were applied to classify congestion levels into quiet, moderate, and busy categories, which directly influence gameplay modes. Experimental results show that PROMETHEE rankings vary across maps (e.g., A6 ranked highest in quiet mode, while B2 dominated in busy mode), demonstrating the system’s contextual adaptability. Usability testing with 50 participants yielded an average system usability scale (SUS) score of 78.9, while expert evaluation using game design factor questionnaire (GDFQ) produced a mean of 4.19/5, both indicating high acceptance and positive user experience. These findings confirm that PROMETHEE is effective in generating context-aware recommendations, providing both strategic depth and engagement. The study concludes that integrating traffic data into serious game design can enrich intelligent transportation systems (ITS) education and awareness, with future improvements possible through real-time player feedback adaptation and machine learning–based traffic prediction.
A hybrid GoogLeNet–GLCM feature extraction framework for textural representation of post-disaster building damage imagery Amani, Holidiyatul; Almais, Agung Teguh Wibowo; Abidin, Zainal; Nugroho, Fresy; Kurniawan, Fachrul; Sugiharto , Tomy Ivan
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v20i1.1214

Abstract

Accurate representation of visual characteristics in post-disaster building imagery is crucial for downstream analytical tasks such as damage interpretation, retrieval, and automated assessment. This study presents a focused investigation of feature extraction using a hybrid approach that integrates deep semantic representations from the GoogLeNet architecture with statistical texture descriptors inspired by the Gray-Level Co-Occurrence Matrix (GLCM). The objective of this work is limited strictly to the generation and analysis of semantic–textural feature vectors rather than the development or evaluation of any classification or prediction model. High-level feature maps are obtained from a selected convolutional layer of GoogLeNet, after which statistical texture properties—contrast, energy, and homogeneity—are computed per channel. A representative set of feature channels is analyzed to demonstrate the capabilities of the proposed hybrid extraction pipeline. The results demonstrate the potential of semantic–textural descriptors to provide interpretable feature characteristics in building-damage imagery. This study provides a methodological foundation and analytical insight for future works that may incorporate these feature representations into classification, clustering, or decision-support frameworks.
Co-Authors Adnan Muhammad Taufiqulhakim Afiifah Zain Raidah Agung Teguh Wibowo Almais Ahmad Fahmi Karami Al Hamidy, Kautsar Quraisy Al Mahdi, Prayuda Zaky Alfarisi, Muhammad Firyal Alfia, Lia Alfia Aljawad, Ulil Albab Amani, Holidiyatul arfianto, farhan dzaffa Arief, Yunifa Miftachul Arif, Yunifa Mifachul Arif, Yunifa Miftahul Asyhari, Hamzah Faizal Atmaja, Gumilang Azhar Affandi Azzahra, Alivia Baihaki, Achmad Fahry Biktarinanda, Arneizha Cahyo Crysdian Dian Maharani, Dian Eko Mulyanto Yuniarno Fachri, Moch Fachrul Kurniawan Fadilaaa, Juniardi Nur Farid Ahmad Sa’aduddin Ferdianto, Akhmad Faizal Ferelian, Muhammad Hammad, Jehad A. H. Hammad, Jehad A.H. Hammad, Jehad Abdelhamid Hammad, Jehad AH Hani Nurhayati Harfianti, Nadya Putri Harto, Sumber Hidayatullah, Fauzil I Gusti Putu Asto Buditjahjanto Ida Ayu Putu Sri Widnyani Ihsan, Afif Nuril Ikhlayel, Mohammed Juniardi Nur Fadila Khomariyah, Aniek Nurul Lestari, Tri Mukti Maharani, Elfira Putri Mahmud, Azkiya Makarim, Muhammad Abyan Marudin, Marudin Mitachul Arif, Yunifa Mochamad Hariadi Mochamad Imamudin Mokhamad Amin Hariyadi Muhamad Husni Mubarok Muhammad Andryan Wahyu Saputra Muhammad Faisal Muhammad Faisal Muhammad Hasan Muhammad Ridho Mutaqin, Ghani Mutaqin, Rizal N, Alfina Nurrahma Najwa Mazaya, Nada Nadhira Nandana, Prana Wijaya Pratama Nazira, Yuzema Mala Novrindah Alvi Hasanah Nuraini, Salsabila Ramadanti Nurrahma ‘N, Alfina Nurrahma, Alfina Nurrahman, Alfina Nurrahma’N, Alfina Pebrianti, Dwi Prakasa, Aji Bagas Pratama, Dicky Arya Prima Astuti Handayani Puspa Miladin Nuraida Safitri A. Basid Putra, Caesar Nafiansyah Raidah, Afiifah Zain Ririen Kusumawati Rohma, Salma Ainur Roro Inda Melani RR. Ella Evrita Hestiandari Sahary, Fitry Taufiq Sa’aduddin, Farid Ahmad Sifaulloh, Hafizzudin Suci Wulandari Suci Wulandari Sugiharto , Tomy Ivan Suhartono Suhartono Suyanta Suyanta Syawab, Moh Husnus Tamaulina Br Sembiring Tarranita Kusumadewi Taufiqulhakim, Adnan Muhammad Utama, Isma Izha Wafiy Anwarul Hikam Yuliawan, Audi Bayu Yuniar Setyo Marandy Yunifa Miftachul Arif Zainal Abidin Zidan, Muhammad Zifora Nur Baiti ’N, Alfina Nurrahma