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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Tika Jurnal Solusi Masyarakat (JSM) Jurnal Ragam Pengabdian
Fadlisyah
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Diagnosa Penyakit Bawang Merah Dengan Metode Forward Chaining Dan Backward Chaining Mukti Qamal; Fadlisyah; Mahara Bengi; Mukarramah
Jurnal Tika Vol 7 No 1 (2022): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.308 KB) | DOI: 10.51179/tika.v7i1.1002

Abstract

Plant diseases are the main enemy of farmers. Many farmers fail to harvest or reduce their agricultural yields because they are not able to properly deal with the diseases that attack their crops. One of the plants that are susceptible to disease is the onion plant. To properly handle the disease that attacks the shallot plant, an agricultural expert is needed. While the number of agricultural experts is limited and unable to deal with the problems of a large number of farmers at the same time, so we need a system that has the capabilities of an agricultural expert, which in this system contains the expertise of an agricultural expert regarding diseases, symptoms and diseases treatment of onion plants. In this study, a Web-based expert system was designed and built using rule-based reasoning with forward chaining and backward chaining inference methods which were intended to assist farmers in diagnosing diseases in shallots, and how to handle them. In this study, forward chaining and backward chaining methods will be compared so that the results will be obtained which method is more suitable for diagnosing a disease. From the results of the comparison analysis of the two methods, it was found that the Forward Chaining method was better and more efficient for diagnosing diseases in shallot plants.
PENERAPAN METODE DECISION TREE CART UNTUK KLASIFIKASI PENYAKIT PADA TANAMAN KELAPA SAWIT: APPLICATION OF THE CART DECISION TREE METHOD FOR CLASSIFYING DISEASES IN OIL PALM PLANTS Alfin Syatriawan; Fadlisyah; Kurniawati
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6544

Abstract

This study developed a web-based oil palm disease detection system to help farmers and related parties quickly and accurately identify diseases based on symptoms observed in the field. The system was built using the CRISP-DM framework, which includes the stages of business understanding, data understanding, data preparation, modelling, evaluation, and implementation. The classification method used is Classification and Regression Tree (CART) due to its ability to handle categorical data and provide easy-to-understand interpretations. The symptom dataset was compiled based on literature references, direct observations, and official data from the North Aceh District Food Crop Agriculture Office and a number of scientific journals. The data consists of 11 main symptoms with a predetermined severity scale. The evaluation results showed excellent model performance, with an accuracy of 95.45%, precision of 97%, recall of 95%, and an F1-score of 95%. The trained model was then integrated into a Flask-based web application, enabling users to input symptoms to obtain disease predictions and management solutions. The novelty of this research lies in the use of relevant local data, the adoption of a symptom-based approach instead of image-based methods, and the integration of the classification model into an applicable web-based system. This system is expected to enhance efficiency, accessibility, and accuracy in decision-making related to disease management.
IMPLEMENTASI METODE CONTENT-BASED FILTERING DALAM REKOMENDASI KEDAI KOPI DI KOTA LHOKSEUMAWE Muhammad Arrayyan; Rozzi Kesuma Dinata; Said Fadlan Anshari; Fadlisyah; Maryana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6641

Abstract

Lhokseumawe a city known for its numerous coffee shops, serves as the focus of this study, which aims to develop a coffee shop recommendation system using a content-based filtering approach based on Google Maps review analysis. A total of 54 coffee shops were collected through web scraping and filtered to 32, as only these shops provided sufficient and relevant reviews according to the selected keywords. User reviews were processed through preprocessing, TF-IDF weighting, and cosine similarity to measure the alignment between user preferences and shop characteristics. A scenario-based evaluation was conducted by using keywords such as “noodles,” “parking,” “spacious,” “toilet,” and “watching together” to represent user preferences. The results show that the system generates recommendations consistent with the presence and relevance of these keywords, with shops such as AN Coffee and Arabica Kopi frequently appearing as top suggestions. Although the evaluation is limited to scenario-based testing, the system demonstrates potential in assisting users in selecting suitable coffee shops. Future work may include hybrid filtering, machine learning methods, automated keyword extraction through topic modeling, and user-based evaluation to improve recommendation quality.
SISTEM PENGUJIAN HAFALAN AL-QUR'AN SURAT AL-GHASYIYAH MENGGUNAKAN METODE TRANSFORMASI WALSH Fikri Akbar; Fadlisyah; Ar Razi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7721

Abstract

Memorizing the Qur’an is a highly recommended act of worship for Muslims, yet traditional learning methods often face constraints such as limited availability of competent teachers. With technological advancements, innovations to support the memorization process independently, objectively, and efficiently are greatly needed. This research aims to develop and measure the performance of an automated system for testing the memorization of Surat Al-Ghasyiyah (verses 1–26) through voice recognition using the Walsh Transform method. The Walsh Transform is applied to convert voice signals from the time domain to the frequency domain using basis functions valued at +1 and −1, extracting unique features from each verse to be compared with reference voice samples. The system was tested on all 26 verses using 6 training voice samples and 4 test voice samples per verse (total 260 samples: 156 training and 104 test). System performance was evaluated using four variations of probability constants: 0.3, 0.4, 0.5, and 0.6. Results indicate that the probability constant significantly affects system accuracy. Detection rates achieved were 70.2% (constant 0.3), 84.6% (constant 0.4), 89.4% (constant 0.5), and peaked at 93.3% (constant 0.6). With an overall average detection rate of 84.4%, it is concluded that the Walsh Transform method is highly effective and the developed system has potential as a reliable aid for Qur’an memorizers.
Peningkatan Kapasitas Pelatih Perisai Diri dalam Persiapan Kejuaraan Asrianda; Kurniawati; Patmono Wibowo; Fadlisyah; Zulfadli
Jurnal Solusi Masyarakat (JSM) Vol. 4 No. 1 (2026)
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jsm.v4i1.26074

Abstract

Perisai Diri silat championships require athletes to demonstrate comprehensive readiness encompassing physical, mental, and strategic dimensions, positioning coaches as key actors in systematically directing the athlete development process. However, limited access to capacity-building programs grounded in scientific approaches remains a challenge for many coaches, particularly in managing psychological aspects, injury prevention, and the application of effective and safe training methods. This community engagement activity aims to enhance the capacity of Perisai Diri coaches in preparing athletes for competition by strengthening their understanding of psychological training, mastery of structured training methods, and the application of health and nutrition principles for athletes. The activity was implemented through integrated stages, including training sessions, mentoring, and evaluation. The results indicate a noticeable improvement in coaches’ understanding and readiness to design adaptive training programs that balance physical and mental training while giving due attention to recovery and nutritional aspects. Strengthening coaches’ capacity contributes to meeting athletes’ needs and supports sustainable improvements in performance and competitive achievement in Perisai Diri silat.
Internet of Things Based Detection System for Pencak Silat PSHT Basic Technique Movements Using the Support Vector Machine Method Ayunda Putri; Zara Yunizar; Muhammad Fikri; Fadlisyah; Hafiz Al Kautsar Aidilof
Jurnal Ragam Pengabdian Vol. 3 No. 2 (2026): Mei-Agustus, Sustainable Development Goals (SDGs): Multidisciplinary Perspectiv
Publisher : Lembaga Teewan Journal Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/j3rq4t73

Abstract

The utilization of technology in sports has become a crucial need to enhance modern coaching efficiency. In pencak silat, particularly within Persaudaraan Setia Hati Terate (PSHT), accurate mastery of basic techniques is essential. However, when students practice independently without supervision, they often struggle to ensure correct hand movements, lowering training quality and increasing injury risks. As a solution, this study develops a real-time, Internet of Things (IoT)-based hand movement monitoring system using Inertial Measurement Unit (IMU) sensors. The sensor data is processed via a Machine Learning approach utilizing a 7-SVM Pipeline architecture. The Support Vector Machine (SVM) algorithm is applied to Model 0 (Movement Classifier) for movement classification, and Models 1–6 (Correctness Classifier) to evaluate quality into "Correct" or "Incorrect". The model is tested using the Leave-One-Subject-Out (LOSO) Cross-Validation method. Results show that Model 0 recognizes movement types with a 63.3% accuracy on unseen subjects. Meanwhile, the correctness models yield varying results; the highest achievement reaches 100% for the Left Jab, whereas the lowest is 55% for the Right Combination due to subjects' biomechanical variations. The results are displayed on a website dashboard as an objective companion tool for independent training while supporting digitalization in preserving pencak silat culture.  
Co-Authors Alfin Syatriawan Ar Razi Asrianda Asrianda Ayunda Putri Fikri Akbar Hafiz Al Kautsar Aidilof Kurniawati Kurniawati Mahara Bengi Maryana Maryana, Maryana Muhammad Arrayyan Muhammad Fikry Mukarramah Mukti Qamal Patmono Wibowo Rozzi Kesuma Dinata Said Fadlan Anshari Zara Yunizar Zulfadli Zulfadli