Article Per Year (5 Year)
p-Index From 2021 - 2026
5.554
P-Index
This Author published in this journals
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 SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL 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 Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Transportation Other

Published : 47 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 47 Documents
Search

 1   2   3   4   5 
CNN Modeling for Classification of Bugis Traditional Cakes Iskandar, Imran; Jeffry, Jeffry; Fadliana, Nurul; Rimalia, Watty; Ahyana, Nurul
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.v6i1.1685

Abstract

Abstract This research aims to create a classification system that can recognize traditional Bugis cakes using the Convolutional Neural Network method. (CNN). Traditional Bugis cakes play an important role in Indonesia's culinary heritage, which is rich in diversity and flavor. However, the lack of documentation and sufficient recognition of these cakes could lead to the loss of cultural knowledge. In this study, a collection of images of traditional Bugis cakes was gathered and processed for training a CNN model. This model was created to recognize and classify various types of cakes based on their visual attributes. The evaluation results show that the CNN model can achieve a high level of accuracy in identifying these cakes, making it a useful tool in preserving and promoting traditional Bugis cakes. This research is expected to contribute to the development of image recognition technology and raise public awareness about the richness of local culinary heritage. Keywords : Convolutional Neural Network (CNN), Bugis Cake, Indonesian Cuisine
PENERAPAN GREY WOLF OPTIMIZER DALAM PELATIHAN MULTI LAYER PERCEPTRON UNTUK MENANGANI MASALAH KLASIFIKASI DAN REGRESI Azis, Azminuddin I. S.; Santoso, , Budy; Jeffry, Jeffry
Advances in Computer System Innovation Journal Vol. 2 No. 3: Desember 2024, ACSI Journal
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/acsijournal.v2i3.653

Abstract

Algoritma Grey Wolf Optimizer (GWO) merupakan salah satu metode metaheuristik terkini yang telah terbukti mampu menunjukkan kinerja yang handal dalam memecahkan berbagai masalah optimasi, terutama dalam mengoptimalkan parameter pada algoritma-algoritma Machine Learning. Salah satu algoritma Machine Learning yang populer adalah Multi Layer Perceptron (MLP), merupakan salah satu varian dari Artificial Neural Network (ANN) yang memiliki parameter weight dan bias yang sensitif terhadap kinerja modelnya. Oleh karenanya, GWO diterapkan untuk mengoptimalkan inisialisasi awal weight dan bias dalam pelatihan MLP untuk meningkatkan kinerja modelnya. Hasil eksperimen ini menunjukan bahwa optimalisasi GWO mampu meningkatkan kinerja MLP, baik pada klasifikasi Iris yang akurasinya meningkat sebesar 33.33% dan pada regresi Silica dengan RMSE yang menurun sebesar 0.1488.
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. 
Co-Authors A Suyuti, Muh Zulfadli Abasa, Sustrin Abdullah, Khairal Adriani, Ika Reskiana Aljabar, Andi Almuhajir Haris, Almuhajir Andi Taufiqurrahman Akbar Andri Dwi Utomo Anirwan Anirwan 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 Manazil, Qornul Marcel Marcel Mardewi Mardewi Mardewi, Mardewi Mohd, Tengku Mon, Muhammad Donal Nasruddin Nasruddin Nipsa Asdiana Supak Nur Ayu Asrhi Nur Ayu Asrhi Nurdyansa Nursalim Nurul Ahyana Nyimas Artina Panggabean, Benny Leonard Enrico Putra Yuzi Bachmid, Fadel Muhammad Qamal Qamal 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 Waluyo, Safril Rizki Wibowo. K, Trisno Wungo, Supriyadi La Yeti Kule Yunendar, Wakhid