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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JMM (Jurnal Masyarakat Mandiri) ILKOM Jurnal Ilmiah JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) AL-ULUM: JURNAL SAINS DAN TEKNOLOGI Jurnal Teknologi Terpadu Journal of Applied Engineering and Technological Science (JAETS) Journal of System and Computer Engineering Jurnal Informatika Terpadu Indonesian Journal of Intellectual Publication (IJI Publication) Global Abdimas: Jurnal Pengabdian Masyarakat Jurnal Ilmu Komunikasi Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK) Madani: Multidisciplinary Scientific Journal Journal Pharmacy and Application of Computer Sciences Advances in Computer System Innovation Journal (ACSI Journal) Indonesian Journal of Intellectual Publication (IJI Publication)
Syahrul Usman
Universitas Pancasakti Makassar, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Penerapan Metode Certainty Factor dan Forward Chaining pada Sistem Pakar Untuk Mendiagnosa Penyakit Ginjal Jeffry, Jeffry; Usman, Syahrul
Indonesian Journal of Intellectual Publication Vol. 1 No. 1 (2020): Nopember 2020, IJI Publication
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/ijipublication.v1i1.35

Abstract

Ilmu komputer yang mempelajari kemampuan komputer untuk bertindak dan memiliki kecerdasan seperti manusia dikenal sebagai kecerdasan buatan, yang termasuk dalam kecerdasan buatan antara lain: penglihatan komputer, pengolahan bahasa alami, robotika, jaringan syaraf tiruan, sistem pakar (expert system). Penelitian ini bertujuan untuk membuat suatu sistem pakar yang digunakan untuk mendiagnosa penyakit ginjal, dimana pengguna bisa mendiagnosis sendiri (skrining mandiri) berdasarkan gejala yang dirasakannya. Pengetahuan pada sistem direpresentasikan dalam bentuk aturan dan metode penalaran yang digunakan adalah metode runut maju (forward chaining) sedangkan nilai kepastian terhadap penyakit menggunakan metode certainty factor yaitu diperoleh dari kombinasi nilai dari user dan pakar. Hasil penelitian menunjukkan bahwa sistem ini mampu mendiagnosa kemungkinan jenis penyakit ginjal yang diderita oleh user dengan menampilkan besaran kepercayaan dari tiap-tiap penyakit. Dari hasil percobaan diperoleh bahwa nilai certainty factor pada Nefritis tubulointerstisial sebesar 0,7502, untuk Sistitis Interstisial sebesar 0,7308, Kanker Kandung Kemih sebesar 0,6429. Sehingga nilai CF terbesar merupakan keputusan dari sistem pakar ini. Besarnya nilai kepercayaan tersebut merupakan hasil perhitungan dengan menggunakan metode certainty factor.
Implementasi Algoritma Machine Learning untuk Forecasting Demand Pada Usaha Kerupuk Sehat Krusawi Wijaya, Neti Septi; Usman, Syahrul; Iskandar, Imran; Rimalia, Watty; Syam, Rahmat Fuady
Madani: Jurnal Ilmiah Multidisiplin Vol 4, No 1 (2026): February 2026
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18358038

Abstract

The rapid development of information technology has encouraged business actors to utilize data analysis to improve efficiency and competitiveness, one of which is through demand forecasting. This study aims to implement machine learning algorithms to forecast product demand in the Krusawi Healthy Crackers business. The method employed is Prophet, which was selected due to its capability to handle time series data with nonlinear trends and seasonal patterns. The data used consist of historical daily sales data from April to July 2024, which were subsequently aggregated into weekly data. The research stages include data collection, data preprocessing (data aggregation, handling missing values, and Box-Cox transformation), Prophet model design with logistic growth and custom bi-monthly seasonality, model training, and performance evaluation. The results indicate that the Prophet model provides excellent forecasting performance, achieving a Mean Absolute Percentage Error (MAPE) of 6.57% or an accuracy level of 93.43%. The model successfully captures trend and seasonal patterns in Krusawi product sales. Therefore, the implementation of machine learning algorithms using the Prophet method proves to be a reliable solution for supporting production planning and inventory management in the Krusawi healthy crackers business, and has the potential to improve operational efficiency and business decision-making.
Detection of Persistent vs. Non-Persistent Drugs in Pharmacy Using Decision Tree Classification Based on Gini, Entropy, and Log Loss Criteria Mardewi, Mardewi; Aziz, Firman; Usman, Syahrul; Fuadi Syam, Rahmat
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2585.186-195

Abstract

This study evaluates the performance of Decision Tree methods in classification, utilizing three different criteria: Entropy, Gini, and Log Loss. The objective is to determine which criterion is most effective in achieving high classification accuracy using prescription data from the UCI repository, comprising 3,424 prescription records with 67 variables. The analysis results show that the Entropy criterion delivers the best performance with an accuracy of 79.1%, followed by the Gini criterion at 78%, and the Log Loss criterion at 77.9%. These findings indicate that the Entropy criterion is superior in reducing uncertainty and capturing the underlying data structure, while both Gini and Log Loss criteria also provide competitive, though slightly lower, results. The main contribution of this research is a comparative evaluation of decision tree criteria using real-world prescription data to support accurate classification of medication adherence, which can be beneficial for developing intelligent pharmacy systems. This research offers valuable insights into the effectiveness of various criteria within the Decision Tree method and can aid in selecting the most appropriate criterion for future classification applications.
Spatio-Temporal Graph Neural Network Based on Nonlinear Time–Frequency Features for Mu-ERD Classification in Multi-Session EEG Motor Imagery Firman Aziz; Jeffry Jeffry; Syahrul Usman; Rahmat Fuadi Syam; Muhammad Nur Arafah; Nurul Fathanah Mustamin
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.8679

Abstract

Mu rhythm event-related desynchronization (ERD) is a key indicator of motor imagery activity based on EEG signals. However, accurate classification of ERD remains challenging due to the nonlinear nature of EEG signals and inter-session variability. This study proposes a motor imagery classification approach using a Spatio-Temporal Graph Neural Network (ST-GNN) model that leverages nonlinear time-frequency features extracted via Variational Mode Decomposition (VMD) and Synchrosqueezing Transform (SST). The dataset was collected from a single healthy subject across five separate sessions, each consisting of two conditions: relaxation and motor imagery. After preprocessing and segmentation, features were extracted and represented as spatio-temporal graphs to be processed by the ST-GNN. The model was evaluated using metrics such as accuracy, F1-score, AUC-ROC, and the Session Stability Index (SSI). The results show that the ST-GNN achieved an accuracy of 94.2%, F1-score of 94.1%, and AUC-ROC of 96.1%, along with high prediction stability across sessions. This performance outperformed baseline models including CNN, CSP+SVM, and STFT+MLP.These findings support the hypothesis that ERD is a distributed brain network phenomenon and demonstrate that the ST-GNN approach with VMD/SST-derived features is a promising strategy for developing adaptive and accurate BCI systems.
Sistem Pendukung Keputusan Penentuan Destinasi Objek Wisata Dengan Metode Simple Additive Weighting (SAW) Berbasis Web Jeffry jeffry; firman aziz; syahrul usman
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.1339

Abstract

One of the biggest regional proceeds of the North Toraja Regency comes from the utilization of tourist objects as recreational objects whether for the local communities or the overseas. However, the lack of information and the lack of systems technology in Toraja destination caused many tourists to visited a few of the many tourism objects available. This problem causes tourists to tend to visit only a fraction of the many tourism objects. Based on these problems, we need a system that helps provide information and determine tourist objects suitable for each tourist, and the tour is more varied. This study produces a decision support system for selecting tourism objects in North Toraja using the “Simple Additive Weighting” method based on a website in the goal of assisting tourists to determine tourist place
Penerapan Tesseract OCR untuk Validasi Pembayaran Otomatis dalam E-Commerce Annisa Salsabila Apriliya Wijaya; A Inayah Auliyah; Jeffry Jeffry; Firman Aziz; Syahrul Usman
Journal of System and Computer Engineering Vol 7 No 2 (2026): JSCE: April 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i2.2625

Abstract

The rapid expansion of e-commerce in Indonesia has resulted in a significant increase in digital transactions, necessitating expedited and precise payment verification. Administrators at the SweetJab hijab e-commerce platform must manually verify bank transfer receipts, a process that is time-consuming and susceptible to errors. This study utilises Optical Character Recognition (OCR) with the Tesseract engine as a supplementary approach for verifying transfer payments on the SweetJab website. The methodology encompasses image preprocessing (resizing to 200%, converting to greyscale, and enhancing contrast), employing Tesseract OCR with PSM 6 and an LSTM model for character recognition, and utilising regular expressions (regex) to extract structured transaction data. We employed Black Box Testing and Character Error Rate (CER) computations on 40 preliminary test samples and 40 post-implementation samples to assess the system. The initial test demonstrated an accuracy of 89.5%, which increased to 92.5% upon complete system integration. This study demonstrates that OCR is an effective method for extracting information from payment receipts, while maintaining security through a final manual verification by the administrator.
Analisis Strategi Komunikasi Digital dalam Meningkatkan Pemahaman Masyarakat Desa terhadap Manfaat Program Makan Bergizi Gratis (MBG) Aiayah Aisyah; Syahrul Usman
MUKASI: Jurnal Ilmu Komunikasi Vol. 4 No. 4 (2025): November 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/mukasi.v4i4.5741

Abstract

The Free Nutritious Meal (MBG) program is one of the Indonesian government’s initiatives to address nutritional challenges in rural areas. The program’s success is largely determined by the effectiveness of digital communication strategies used to disseminate information to local communities. This study aims to evaluate the extent to which digital communication strategies enhance rural community understanding of the MBG program. The research employed a qualitative case study approach within a post-positivist paradigm. Data were collected through in-depth interviews, participatory observation, document and digital content analysis, and focus group discussions involving village heads, village operators, community leaders, and MBG beneficiaries. Findings show that the use of WhatsApp group discussions combined with government-issued infographics increased community understanding from 32% to 78% among 50 participants, with higher engagement observed among younger adults. The study further developed an integrated digital communication model that incorporates multi-platform outreach, culturally adapted visual messages, and two-way feedback mechanisms. The main contribution of this research lies in providing a validated framework for digital communication in rural contexts. Practically, the study recommends leveraging familiar social media platforms, providing digital literacy training for village operators, and strengthening feedback mechanisms to optimize program dissemination
Co-Authors A Inayah Auliyah Abasa, Sustrin Aiayah Aisyah Almuhajir Haris, Almuhajir Anirwan Anirwan Annisa Salsabila Apriliya Wijaya Arafah, Muhammad Nur Firman Aziz Fuadi Syam, Rahmat Hamdani Nur, Nur Iskandar, Imran Ismail Ismail Jeffry Jeffry Jeffry Kahar Gani La Wungo, Supriyadi Lutfi Budi Ilmawan, Lutfi Budi M Rezky Armansyah Mardewi Mardewi Mardewi, Mardewi Muh Halqi Ashar Monoarfa Muhammad Arfah Asis Muhammad Lutfi Nur Ayu Asrhi Nur Ayu Asrhi Nurdyansa Nurul Fathanah Mustamin Putra Yuzi Bachmid, Fadel Muhammad Qamal Qamal Rahmat Fuadi Syam Rijal, Muhammad Rimalia, Watty Rofiq Harun Sasmita, Dhilan Sudirman Melangi Sumardi Sumardi Syam, Rahmat Fuadi Syam, Rahmat Fuady Taufik, Akbar Wijaya, Neti Septi Wungo, Supriyadi La