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All Journal International Journal of Electrical and Computer Engineering Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JMM (Jurnal Masyarakat Mandiri) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Almana : Jurnal Manajemen dan Bisnis 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 Publikasi Riset Mahasiswa Manajemen Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) Journal Pharmacy and Application of Computer Sciences Journal of Innovative and Creativity Advances in Computer System Innovation Journal (ACSI Journal) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Indonesian Journal of Intellectual Publication (IJI Publication)
Jeffry Jeffry
Prodi Ilmu Komputer, Institut Teknologi Bacharuddin Jusuf Habibie
Agriculture, Biological Sciences & Forestry 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 Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Transportation Other

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INTELLIGENCE SOCIAL MEDIA ANALYTICS PADA PEMERINTAH KOTA MAKASSAR PERIODE AGUSTUS-SEPTEMBER 2023 Anwar; Nursalim; jeffry, jeffry
Journal Pharmacy and Application of Computer Sciences Vol. 2 No. 1: Februari: 2024: JOPACS
Publisher : Arlisaka Madani Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59823/jopacs.v2i1.51

Abstract

Perkembangan teknologi informasi dan komunikasi, media sosial telah menjadi salah satu sumber utama informasi dan wadah ekspresi masyarakat, warga Makassar aktif berpartisipasi dalam berbagai platform media sosial seperti Facebook dan Instagram, menjadikannya sumber data yang berharga untuk memahami pandangan, kebutuhan, dan isu-isu yang sedang berkembang dalam komunitas. Dengan menggunakan teknik web scraping dan API (Application Programming Interface) untuk pengumpulan data, teknik analisis data meliputi analisis sentiment, analisis temporal untuk mengidentifikasi tren, analisis jaringan sosial untuk memahami hubungan antar entitas di media sosial, dan analisis tekstual untuk mengidentifikasi topik atau entitas penting dalam teks dengan pengkasifikasian menggunakan algoritma Naïve bayes. Dalam periode Agustus sampai September 2023, ditemukan sentiment positif sebesar 55,07%, sentiment negatif 21,01%, sentiment netral 23,92 dari jumlah post 2.666, jumlah interaksi 110.25, dengan melibatkan 754 akun yang berpartisipasi dalam berbagai isu yang dianalisis
Analisis Rencana Usaha Mr.Style Barbershop Dengan Konsep Layanan Booking Appointment Berbasis Digital di Kota Palembang Jeffry, Jeffry; Artina, Nyimas
Publikasi Riset Mahasiswa Manajemen Vol 4 No 2 (2023): Publikasi Riset Mahasiswa Manajemen
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/prmm.v4i2.4307

Abstract

Mr.Style Barbershop adalah sebuah bisnis yang bergerak dibidang jasa yang menawarkan 2 layanan yaitu jasa home servis dan langsung ke tempat barbershop. Dengan spesialisasi pada jasa potong rambut, cukur dan pewarnaan rambut. Usaha ini berlokasi diJalan Musi Raya Barat No. 11 dalam bentuk ruko. Berdasarkan aspek kelayakan usaha, Mr.Style Barbershop dinyatakan layak untuk dijalankan dan memiliki prospek yang mengguntungkan di masa mendatang.
Detection of Persistent vs Non-Persistent Medications in Pharmacy Using Artificial Intelligence: Development of Intelligent Algorithms for Pharmaceutical Product Safety Abasa, Sustrin; Aziz, Firman; Ishak, Pertiwi; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1618

Abstract

The pharmaceutical industry requires an effective system to detect medications that are persistent and non-persistent, in order to improve safety and the efficiency of product management. This study aims to develop a system based on Artificial Intelligence (AI) using the Decision Tree algorithm to classify medications based on prescription data provided by doctors. The dataset used in this study includes prescription information, such as medication type, prescription quantity, frequency of use, and duration of medication use, which are used to determine whether the medication is persistent or non-persistent. The Decision Tree algorithm is applied to develop a reliable classification model, with the goal of detecting medications that are used continuously (persistent) and those that are not used on a continuous basis (non-persistent). This study applies AI technology in the pharmaceutical field, focusing on the use of doctor prescriptions and classifying medications based on usage characteristics. The results of the study show that the algorithm performs well with an accuracy of 78.33%, recall of 0.7804, precision of 0.7804, and an F1 score of 0.6934, indicating the model's ability to classify medications with reasonable accuracy.
Classification of Chocolate Consumption Using Support Vector Machine Algorithm Aziz, Firman; Jeffry, Jeffry; Ayu Asrhi, Nur; La Wungo, Supriyadi
Journal of System and Computer Engineering Vol 6 No 2 (2025): JSCE: April 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i2.1860

Abstract

Chocolate, derived from the processing of cocoa beans (Theobroma cacao), is a widely consumed product with potential health risks when consumed excessively. This study investigates the classification of chocolate consumption behaviors using the Support Vector Machine (SVM) algorithm and evaluates its classification performance. A benchmark dataset on chocolate consumption was employed, partitioned into nine folds for training and testing purposes. To mitigate issues related to data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The experimental findings indicate that SVM, enhanced by SMOTE, demonstrates a reliable capacity for classifying chocolate consumption categories. Performance evaluation across multiple experiments revealed variations in Accuracy, Precision, Recall, and F1-Score, with overall accuracies ranging from 50% to 60%, suggesting moderate but consistent classification performance.
Sentiment Analysis in Indonesian’s Presidential Election 2024 Using Transfomer (Distilbert-Base-Uncased) Aljabar, Andi; Karomah, Binti Mamluatul; Tarisafitri, Nahla; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 2 (2025): JSCE: April 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i2.1867

Abstract

Utilizing a transformer-based natural language processing model called DistilBERT-base-uncased, this study investigates the use of sentiment analysis in relation to Indonesia's 2024 presidential election. Particularly during political events, sentiment analysis is a potent tool for gaining insight into public opinion. The program divides public posts' sentiment into positive and negative categories by examining social media data (twitter). In order to assure consistency and correctness, the dataset used in the research has been carefully selected. DistilBERT is then used to train the model. The result shows from 19920 row of data only 4.47% of Indonesia’s citizen left positive comment.
Sistem Deteksi Kekeruhan Air Berbasis Citra Digital Menggunakan Gaussian Filtering dan Thresholding jeffry, jeffry
Indonesian Journal of Intellectual Publication Vol. 5 No. 2 (2025): Maret 2025, IJI Publication
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/ijipublication.v5i2.696

Abstract

Penelitian ini bertujuan untuk mengidentifikasi tingkat kekeruhan air menggunakan metode pengolahan citra digital berbasis MATLAB. Sebanyak 10 sampel air dengan tingkat kekeruhan yang bervariasi dianalisis menggunakan dua pendekatan, yaitu pengukuran manual menggunakan TDS meter dan pengolahan citra digital melalui tahapan konversi RGB, Gaussian filtering, thresholding, serta analisis histogram nilai piksel. Hasil pengukuran menunjukkan pola hubungan berbanding terbalik antara nilai intensitas piksel citra dan tingkat kekeruhan air dalam satuan PPM. Misalnya, pada Sampel 1 dengan tingkat kekeruhan 52 PPM diperoleh nilai piksel sebesar 56,821, sedangkan pada Sampel 10 dengan kekeruhan tertinggi yaitu 83 PPM, nilai piksel turun menjadi 11,749. Secara umum, tren ini konsisten pada seluruh sampel, menunjukkan bahwa semakin tinggi tingkat kekeruhan air, semakin rendah nilai piksel yang dihasilkan. Temuan ini membuktikan bahwa pendekatan berbasis pengolahan citra digital dapat digunakan sebagai metode alternatif yang efisien dan praktis untuk mendeteksi tingkat kekeruhan air secara kuantitatif
Performance Exploration of Tree-Based Ensemble Classifiers for Liver Cirrhosis: Integrating Boosting, Bagging, and RUS Techniques Aziz, Firman; Jeffry, Jeffry; Wungo, Supriyadi La; Rijal, Muhammad; Usman, Syahrul
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2031

Abstract

Liver cirrhosis, as a significant chronic liver disease, exhibits a rising global prevalence, demanding more effective preventive approaches. In an effort to enhance early detection and patient management, this research proposes the development of a liver cirrhosis risk prediction model using machine learning technology, specifically comparing the performance of three ensemble tree models: Ensemble Boosted Tree, Ensemble Bagged Tree, and Ensemble RUSBoosted Tree. Utilizing clinical and laboratory data from adults with a history or risk of cirrhosis, the study reveals that Ensemble Bagged Tree achieved the highest accuracy at 71%, followed by Ensemble Boosted Tree (67.2%) and Ensemble RUSBoosted Tree (66%). Analysis of clinical and laboratory variables provides further insights into the most significant contributors to risk prediction. The findings lay the groundwork for the advancement of a more sophisticated liver cirrhosis risk prediction tool, supporting a vision of more personalized and effective preventive strategies in liver disease management
OPTIMALISASI MANAJEMEN KEHADIRAN DENGAN SISTEM ABSENSI IOT BERBASIS RFID DAN ANALISIS AKTUARIA Utomo, Andri Dwi; Akbar, Andi Taufiqurrahman; Syafaat, Muhammad; Jeffry, Jeffry; A Suyuti, Muh Zulfadli; Adriani, Ika Reskiana
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i2.29748

Abstract

Abstrak: Di era digital, salah satu tantangan organisasi masyarakat adalah pengelolaan data kehadiran yang masih dilakukan secara manual, sehingga kurang mendukung analisis berbasis data. Untuk mengatasi masalah ini, program pengabdian kepada masyarakat ini bertujuan memberikan pelatihan mengenai sistem absensi berbasis Internet of Things (IoT) dengan data logger, serta analisis data menggunakan pendekatan aktuaria. Pelatihan ini bertujuan untuk meningkatkan hard-skills peserta dalam hal pemahaman dan penerapan teknologi IoT, konfigurasi perangkat, serta analisis data. Selain itu, pelatihan juga berfokus pada peningkatan soft-skills peserta dalam hal pemecahan masalah, kolaborasi tim, dan pengambilan keputusan berbasis data, yang akan berguna dalam implementasi sistem absensi secara mandiri. Kegiatan ini melibatkan Study Club Informatika Parepare sebagai mitra, dengan 22 peserta. Metode yang digunakan mencakup pengenalan teknologi IoT, praktik langsung, dan analisis data. Pelatihan terdiri dari pemahaman dasar IoT, konfigurasi perangkat, serta integrasi sistem dengan Google Spreadsheet untuk pencatatan data absensi secara otomatis. Hasil evaluasi menunjukkan peningkatan signifikan dalam pemahaman peserta, dengan rata-rata nilai pretest 61,52% meningkat menjadi 92,61% pada posttest. Implementasi sistem ini membantu organisasi dalam digitalisasi proses absensi, meningkatkan efisiensi administrasi, dan membuka peluang penerapan lebih luas di komunitas lainnya.Abstract: In the digital era, one of the challenges faced by community organizations is attendance data management, which is still done manually and does not adequately support data-driven analysis. To address this issue, this community service program aims to provide training on Internet of Things (IoT)-based attendance systems using data loggers, along with data analysis using an actuarial approach. This training aims to enhance the participants' Hard-Skills in understanding and applying IoT technology, device configuration, and data analysis. Additionally, the training focuses on improving the participants' soft skills in problem-solving, teamwork, and data-driven decision-making, which will be useful in the independent implementation of the attendance system. This program involves Study Club Informatika Parepare as a partner, with 22 participants. The methods used include IoT technology introduction, hands-on practice, and data analysis. The training covers basic IoT concepts, device configuration, and system integration with Google Spreadsheet for automated attendance recording. Evaluation results indicate a significant improvement in participants' understanding, with an average pretest score of 61.52% increasing to 92.61% in the posttest. The implementation of this system helps organizations digitize attendance processes, improve administrative efficiency, and expand its potential applications to other communities. 
Integrating Bayesian Optimization into Ensemble Logistic Regression for Explainable AI-Based Customer Behavior Analysis Jeffry, Jeffry; Azis, Azminuddin I. S.; Kandakon, Elisabeth Tri Juliana
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15219

Abstract

Understanding customer behavior is a strategic factor in business decision-making, particularly within the automotive sector, where competition is intense and product variety is diverse. While previous studies often rely on limited demographic variables, such as age and gender, this research advances the field by integrating ensemble logistic regression with Bayesian Optimization for hyperparameter tuning and SHAP-based interpretability. The proposed model incorporates additional features beyond demographics, including vehicle category, product type, vehicle year, dealer branch, and transaction source, to enhance predictive accuracy. The methodology involves data preprocessing through encoding and cleaning, class balancing using SMOTE combined with undersampling, and stratified train-test splitting (80:20). Baseline Logistic Regression achieved an accuracy of 80%, ROC AUC of 0.89, precision of 0.47/0.96, recall of 0.84/0.79, and F1-scores of 0.59/0.89. By applying ensemble logistic regression with Bayesian Optimization, performance improved to 84% accuracy, ROC AUC of 0.92, precision of 0.51/0.98, recall of 0.83/0.84, and F1-scores of 0.63/0.92. SHAP analysis confirmed that the additional features significantly contribute to prediction outcomes. The novelty of this study lies in combining Ensemble Logistic Regression with Bayesian Optimization and SHAP explainability in the automotive domain, offering not only improved accuracy but also interpretability and fairness for business decision-making, providing actionable insights for targeted marketing strategies and product management. Future studies may incorporate broader behavioral and transactional variables to capture more nuanced customer decision patterns..
A Deep Learning Approach to Respiratory Disease Classification Using Lung Sound Visualization for Telemedicine Applications Wahyudi, Andi Enal; Batau, Radus; Aziz, Firman; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 4 (2025): JSCE: October 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i4.2144

Abstract

This study presents the development of an intelligent system for the classification of respiratory diseases using lung sound visualizations and deep learning. A hybrid Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN–BiLSTM) model was designed to classify four conditions: asthma, bronchitis, tuberculosis, and normal (healthy). Lung sound recordings were converted into time-frequency representations (e.g., mel-spectrograms), enabling spatial-temporal feature extraction. The system achieved an overall classification accuracy of 99.5%, with F1-scores above 0.93 for all classes. The confusion matrix revealed minimal misclassifications, primarily between asthma and bronchitis. These results suggest that the proposed model can effectively support real-time, non-invasive respiratory screening, particularly in telemedicine environments. Future work includes clinical validation, integration of patient metadata, and adoption of transformer-based models to further enhance diagnostic performance.
Co-Authors A Inayah Auliyah A Suyuti, Muh Zulfadli Abasa, Sustrin Adriani, Ika Reskiana Almuhajir Haris, Almuhajir Andi Aljabar Andi Taufiqurrahman Akbar Andri Dwi Utomo Anirwan Anirwan Annisa Salsabila Apriliya Wijaya Anwar Arafah, Muhammad Nur Ayu Asrhi, Nur Azminuddin I. S. Azis Batau, Radus Binti Mamluatul Karomah Buang, Misbahuddin Enal Wahyudi, Abdi Fadliana, Nurul Firman Aziz Ishak, Pertiwi Iskandar, Imran Ismail Ismail Ispanti Zaman Kahar Gani Kandakon, Elisabeth Tri Juliana Kurniyan Sari, Sri La Wungo, Supriyadi M Rezky Armansyah Marcel Marcel Mardewi Mardewi Mardewi, Mardewi Mon, Muhammad Donal Muhammad Syafaat Nasruddin Nasruddin Nipsa Asdiana Supak Nur Ayu Asrhi Nur Ayu Asrhi Nurdyansa Nursalim Nurul Ahyana Nurul Fathanah Mustamin Nyimas Artina Panggabean, Benny Leonard Enrico Putra Yuzi Bachmid, Fadel Muhammad Qamal Qamal Rahmat Fuadi Syam Rijal, Muhammad Rimalia, Watty Santoso, , Budy Sari, Sri Kurniyan Sasmita, Dhilan Sentoso, Antony Sukarno Abby Sumardi Sumardi Susi Handayani Syafaat, Muhammad Syahrul Usman Syam, Rahmat Fuadi Syam, Rahmat Fuady Tarisafitri, Nahla Velayaty, Ali Akbar Wahyudi, Andi Enal Wibowo. K, Trisno Wungo, Supriyadi La Yeti Kule Yunendar, Wakhid