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All Journal Jatilima : Jurnal Multimedia Dan Teknologi Informasi Indonesian Journal of Innovation Studies Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Komtekinfo Internet of Things and Artificial Intelligence Journal
Rahman, Zumardi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Mathematics

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Implementation of a Forward Chaining Expert System in Diagnosing Laptop Damage Sakinah, Putri; Hendra, Yomei; Satria, Budy; Rahman, Zumardi; Maulana, Fajar; Syaputra, Aldo Eko
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.791

Abstract

Laptops have become a primary need for almost everyone, but the damage rate is also high. Manual diagnosis of laptop damage requires special expertise and is prone to errors that can exacerbate damage. The purpose of this study was to develop an expert system based on the forward chaining method to diagnose laptop damage. Data obtained through expert interviews, literature study, and the internet comprised 13 symptoms and five main types of laptop damage. Relate data in tables to form IF-THEN rules of the forward chaining method. The test results on six symptoms indicate that the system can diagnose IC Power damage with 100% accuracy, which is the highest diagnosis. In conclusion, the forward chaining method can diagnose laptop damage based on emerging symptoms.
Penerapan Metode Simple Additive Weighting dan Fuzzy Logic dalam Menganalisa Mitigasi Risiko Rozakh, Muhammad; Siregar, Diffri; Nurcahyo, Gunadi Widi; Sovia, Rini; Rahman, Zumardi
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.621

Abstract

Risk management is a stage to identify and address risks affecting a system or project. The risk mitigation process takes time and must be carried out periodically to be effective. In the context of education, information technology plays an important role in increasing the speed and accuracy of decision-making, including in risk mitigation. This study aims to apply the Simple Additive Weighting (SAW) and Fuzzy Logic methods to provide recommendations for risk mitigation that must be prioritized in a university environment. This research method uses a combination of Simple Additive Weighting (SAW) and Fuzzy Logic. Starting with using SAW to determine the criteria, weights, and suitability ratings, followed by making a decision matrix and normalization. The ranking data is then processed with Fuzzy Logic to handle uncertainty and produce objective decisions through the formation of a rule-base, inference, and defuzzification. The research dataset consists of 50 risk records and criteria used in the risk mitigation process obtained from the University. The results of the study indicate that the application of DSS using the SAW and Fuzzy Logic methods provides recommendations for risk mitigation with the results of 1 data not recommended for risk mitigation, 8 data highly recommended, and 4 data recommended for mitigation. This study contributes to designing an effective decision support system, allowing university leaders to make appropriate risk mitigation decisions based on relevant and accurate data using the SAW and Fuzzy Logic methods
Sentiment Analysis of Gojek App Reviews on Google Play Store with Natural Language Processing Using Naive Bayes' Algorithm Rahman, Zumardi; Sakinah, Putri; Hendra, Yomei; Satria, Budy; Maulana, Fajar; Ayun, Aisyah Qurrata
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 6 No. 03 (2024): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v6i03.1189

Abstract

In the digital era, sentiment analysis is an important tool to understand user perceptions of applications, including the Gojek application. This study aims to analyze the sentiment of Gojek application user reviews on the Google Play Store using the Naive Bayes algorithm. The research process involved collecting 5,000 reviews, preprocessing the text, weighting with TF-IDF, and applying the Naive Bayes algorithm to classify sentiment into negative, neutral, and positive. The evaluation results show that the model has the best accuracy of 76% after applying the data balancing technique. The model's performance for negative sentiment is very good with a precision of 91% and an F1 score of 87%. Positive sentiment shows quite good performance with a precision of 76% and an F1 score of 65%. However, neutral sentiment has low precision (23%) although recalls increased to 51%. Sampling techniques such as SMOTE have succeeded in improving the model's ability to recognize underrepresented classes. With an overall evaluation of weighted average precision of 82% and an F1 score of 78%, this model is considered quite reliable in analyzing the sentiment of Gojek app reviews. This research provides insights for application developers in improving service quality based on user perception..
Optimalisasi Akreditasi Perguruan Tinggi dengan Orkestrasi Business Intelligence Berbasis K-Means dan OLAP Sakinah, Putri; Eko Syaputra, Aldo; Rahman, Zumardi; Fajri, Muhammad; Fatwa Rachmansyah, Haikal
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 3 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i3.1443

Abstract

University accreditation is a key indicator in assessing the quality and competitiveness of higher education institutions. A high accreditation score reflects strong academic standards, institutional competitiveness, and trust from students, industry, and government. However, the accreditation process is highly complex as it involves multiple aspects, such as curriculum, student performance, faculty competence, as well as the quality of research and community service. Universities often face challenges in managing, analyzing, and presenting academic data systematically, which results in suboptimal strategic decision-making for improving accreditation. The limited use of Business Intelligence (BI) in academic data analysis reduces the effectiveness of identifying the main factors influencing institutional performance. This study aims to develop an academic analysis model based on BI by integrating K-Means Clustering to group academic performance according to specific patterns, Silhouette Score to evaluate clustering quality, and Online Analytical Processing (OLAP) to present academic data in an interactive multidimensional form. The research data were obtained from the Institute for Quality Assurance and Educational Development (LPPPM), covering faculty, student, research, and community service data, as well as other accreditation indicators. The research method includes data collection and preprocessing, application of K-Means for clustering, evaluation using Silhouette Score, and the development of an OLAP dashboard for exploring academic data across relevant accreditation dimensions. The results of the study show that the K-Means method with k = 2 produces the most optimal grouping of study program academic performance based on the highest Silhouette Score value, which is then successfully visualized multidimensionally through OLAP to clarify the distribution, patterns, and differences in characteristics between clusters in an interactive dashboard. Keywords: Business Intelligence; Higher Education Accreditation; K-Means Clustering; Silhouette Score; OLAP.
Developing a Supply Chain Management Application for Laying Hens with Integrated Egg Quality Detection Based on Computer Vision (Case Study of a Laying Hen MSME) : Pengembangan Aplikasi Manajemen Rantai Pasok untuk Ayam Petelur dengan Deteksi Kualitas Telur Terintegrasi Berbasis Penglihatan Komputer (Studi Kasus Usaha Mikro, Kecil, dan Menengah (UMKM) Ayam Petelur) Bamatraf, Lutfia; Akbar, Rahmad; Rahman, Zumardi; Afifah, Belia; Nurkholis, Nurkholis
Indonesian Journal of Innovation Studies Vol. 27 No. 1 (2026): January
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v27i1.1873

Abstract

Manual egg grading and fragmented supply chain management in poultry SMEs often cause inconsistent egg quality decisions, inaccurate inventory records, and avoidable distribution losses. This study aims to develop and evaluate an integrated supply chain management system that embeds computer vision–based egg quality detection to improve real-time operational control and decision-making. The system is implemented as a digital SCM application connected to a convolutional neural network model trained on 1,200 labeled egg images across five quality categories (good, cracked, dirty, fertile, non-fertile), with performance assessed using accuracy, precision, recall, F1-score, and inference time, alongside before–after operational and economic measurements in an SME workflow. Results show the CNN achieves 92% validation accuracy with average precision 0.92, recall 0.94, F1-score 0.93, and 0.11 seconds per egg inference, enabling practical real-time classification. After integration, egg sorting accuracy increases from 75.5% to 90.2%, inspection time decreases by 81.3%, sorting capacity rises 5.3×, and inventory accuracy improves from 82% to 98%, reducing daily stock discrepancies by 85%. The novelty lies in tightly coupling computer vision quality outputs with SCM inventory and distribution modules, creating immediate stock updates and automated control points. The findings imply that AI-enabled digital supply chain management strengthens quality assurance, inventory optimization, and SME profitability, supporting scalable modernization of food supply chains through deployable computer vision systems. Highlights: Integrated computer vision raises egg sorting accuracy from 75.5% to 90.2%. Real-time CNN classification reaches 92% validation accuracy with 0.11 s inference. Digital SCM lifts inventory accuracy to 98% and delivers 193% ROI payback. Keywords: Computer Visio, Supply Chain Management, Egg Quality Detection, Convolutional Neural Network, Poultry SMEs, Inventory Optimization
Co-Authors Aldo Eko Syaputra Ayun, Aisyah Qurrata Bamatraf, Lutfia Belia Afifah Budy Satria Fatwa Rachmansyah, Haikal Gunadi Widi Nurcahyo Hendra, Yomei Maulana, Fajar Muhammad Fajri Nurkholis Nurkholis Rahmad Akbar Rozakh, Muhammad SAKINAH, PUTRI Siregar, Diffri Sovia, Rini