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All Journal Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Komtekinfo Internet of Things and Artificial Intelligence Journal
Rahman, Zumardi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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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..
Co-Authors Aldo Eko Syaputra Ayun, Aisyah Qurrata Budy Satria Gunadi Widi Nurcahyo Hendra, Yomei Maulana, Fajar Rozakh, Muhammad SAKINAH, PUTRI Siregar, Diffri Sovia, Rini