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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknoinfo Jurnal Nasional Komputasi dan Teknologi Informasi Journal of Information System, Applied, Management, Accounting and Research Jurnal Informatika Ekonomi Bisnis REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Bulletin of Information Technology (BIT) Brilliance: Research of Artificial Intelligence Journal of Social Research Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Mandiri Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Lentera Pengabdian Jurnal Informatika Ekonomi Bisnis Riwayat: Educational Journal of History and Humanities VIDHEAS: Jurnal Nasional Abdimas Multidisiplin RECORD Journal of Loyality and Community Development
Butsianto, Sufajar
Universitas Pelita Bangsa
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Neuroscience Social Sciences Transportation Other

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IMPLEMENTASI PROSES PEMBELAJARAN TERHADAP PARA GURU DENGAN APLIKASI MICROSOFT OFFICE DI SMP IT AL-HIDAYAH Edy Widodo; Sufajar Butsianto; Andriani, Andriani; Amali, Amali
JURNAL PENGABDIAN MANDIRI Vol. 4 No. 6: Juni 2025
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Dalam mengembangkan duia pendidikan itu tidak mudah dan banyak sekali kendala-kendala yang memang harus kita hadapi bersama, terutama terkait dengan begitu pesatnya perkembangan Teknologi Informasi yang saat ini memang sangat dibutuhkan dan sangat berpengaruh dalam dunia pendidikan. SMP IT Al-Hidayah adalah sebuah pendidikan yang berkembang untuk ikut serta dalam memajukan dunia pendidikan di Indonesia dan masyarakat luas. Untuk meningkatkan kreatifitas para guru dilingkungan Al-Hidayah perlu dilakukan pelatihan-pelatihan Microsoft Office secara berkelanjutan untuk meningkatkan pengetahuan dan kreatifitas pendidikan di masa akan datang untuk menunjan era teknologi digitalisasi sehingga pelatihan Microsoft Office di lingkungan SMP IT Al-Hidayah dapat berjalan dengan baik dan lancar
Pelatihan Penggunaan Media Sosial untuk Pemasaran di ASM Insulindo Butsianto, Sufajar; Sulaeman, Asep Arwan; Siswandi , Arif; Setyawan, Wisnu
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 3 No. 1 (2025): Juni 2025
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v3i1.117

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman dan keterampilan mahasiswa serta civitas akademika ASM Insulindo dalam memanfaatkan media sosial sebagai sarana pemasaran yang efektif di era digital. Di tengah pesatnya perkembangan teknologi informasi, media sosial seperti Instagram, TikTok, dan Facebook telah menjadi platform strategis dalam memperluas jangkauan bisnis dan membangun brand awareness. Namun, pemanfaatannya masih belum optimal di kalangan mahasiswa yang memiliki potensi besar sebagai digital marketer. Melalui pelatihan ini, peserta dibekali dengan pengetahuan dasar mengenai digital marketing, strategi konten kreatif, penggunaan fitur-fitur iklan media sosial, serta analisis performa pemasaran digital. Metode yang digunakan meliputi ceramah, diskusi interaktif, praktik langsung, dan studi kasus. Hasil kegiatan menunjukkan peningkatan signifikan dalam pemahaman peserta terhadap teknik pemasaran digital serta kemampuan membuat dan mengelola konten promosi secara mandiri. Diharapkan pelatihan ini dapat menjadi langkah awal dalam menciptakan wirausahawan muda yang adaptif terhadap perkembangan teknologi digital.
Analisis Sentimen Ulasan Aplikasi Jamsostek dengan SVM, Random Forest, dan Logistic Regression Butsianto, Sufajar; Rifa'i, Anggi Muhammad
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1266

Abstract

The digitalization of public services has encouraged the development of the Jamsostek Mobile (JMO) application by BPJS Ketenagakerjaan. This application is expected to provide convenience in accessing information, JHT claims, and other services. However, user reviews on the Google Play Store show diverse perceptions, ranging from satisfaction to technical complaints. This study aims to conduct sentiment analysis on user reviews of the JMO application by classifying opinions into positive, negative, and neutral sentiments. Data were collected through crawling from the Google Play Store and processed using text preprocessing stages, including data cleaning, case folding, stopword removal, tokenization, stemming, and Term Frequency–Inverse Document Frequency (TF-IDF) weighting. The classification process was then carried out using three machine learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Logistic Regression. The results indicate that negative sentiment dominates with 46%, followed by positive sentiment at 40% and neutral at 14%. Most complaints are related to login difficulties, application errors, and technical bugs in claim features. In terms of algorithm performance, SVM with a linear kernel achieved the highest accuracy of 87.5% and an F1-score of 0.87, outperforming Random Forest (85.3%) and Logistic Regression (82.7%). Academically, this study reinforces the effectiveness of SVM in sentiment analysis using TF-IDF, while practically providing recommendations for BPJS Ketenagakerjaan to improve system stability, login speed, and reduce application bugs to enhance user satisfaction.
Analisis Sentimen Ulasan Aplikasi Jamsostek dengan SVM, Random Forest, dan Logistic Regression Butsianto, Sufajar; Rifa'i, Anggi Muhammad
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1266

Abstract

The digitalization of public services has encouraged the development of the Jamsostek Mobile (JMO) application by BPJS Ketenagakerjaan. This application is expected to provide convenience in accessing information, JHT claims, and other services. However, user reviews on the Google Play Store show diverse perceptions, ranging from satisfaction to technical complaints. This study aims to conduct sentiment analysis on user reviews of the JMO application by classifying opinions into positive, negative, and neutral sentiments. Data were collected through crawling from the Google Play Store and processed using text preprocessing stages, including data cleaning, case folding, stopword removal, tokenization, stemming, and Term Frequency–Inverse Document Frequency (TF-IDF) weighting. The classification process was then carried out using three machine learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Logistic Regression. The results indicate that negative sentiment dominates with 46%, followed by positive sentiment at 40% and neutral at 14%. Most complaints are related to login difficulties, application errors, and technical bugs in claim features. In terms of algorithm performance, SVM with a linear kernel achieved the highest accuracy of 87.5% and an F1-score of 0.87, outperforming Random Forest (85.3%) and Logistic Regression (82.7%). Academically, this study reinforces the effectiveness of SVM in sentiment analysis using TF-IDF, while practically providing recommendations for BPJS Ketenagakerjaan to improve system stability, login speed, and reduce application bugs to enhance user satisfaction.
Penerapan Machine Learning untuk Prediksi Kenaikan Harga Beras Premium Menggunakan Algoritma Regresi Linier: Application of Machine Learning for Premium Rice Price Increase Prediction Using Linear Regression Algorithm Widiyatmoko, Arif Tri; Butsianto, Sufajar; Nugroho, Agung
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2123

Abstract

Ketidakstabilan harga beras premium sebagai komoditas pangan pokok memerlukan solusi prediksi yang akurat untuk membantu perencanaan ekonomi. Penelitian ini menerapkan algoritma Machine Learning, yaitu Regresi Linier, untuk memprediksi kenaikan harga beras premium. Model dilatih menggunakan data historis harga dan dievaluasi kinerjanya dengan metrik MAE (0.244), MSE (0.092), dan R-squared (0.893), menunjukkan tingkat akurasi yang cukup baik dalam memprediksi harga. Selanjutnya, model yang berhasil dikembangkan diimplementasikan ke dalam aplikasi web interaktif berbasis Streamlit. Aplikasi ini memungkinkan pengguna untuk memasukkan tanggal dan secara langsung mendapatkan prediksi harga beras premium. Hasil penelitian menunjukkan bahwa Regresi Linier efektif dalam memprediksi harga beras premium, dan implementasi ke dalam aplikasi Streamlit berhasil menyediakan alat prediksi yang mudah diakses. Meskipun demikian, penelitian lanjutan dapat berfokus pada peningkatan akurasi model dan eksplorasi algoritma Machine Learning lainnya untuk prediksi harga komoditas
Sentiment Analysis Of Indosat's Mobile Operator Services On Twitter Using The Naïve Bayes Algorithm Butsianto, Sufajar; Fauziah, Sifa; Naya, Candra; Maulana, Futuh
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4084

Abstract

Twitter is a social media that allows users to share information with others in real time. Information that is shared on Twitter is usually referred to as a tweet. Sentiment analysis is a branch of research in the text mining domain where the process of identifying and extracting sentiment data will usually be categorized based on its polarity, whether it is positive, negative or neutral. We can process data from opinions on Twitter using data mining techniques, namely classification. The algorithm that will be used in this research is the Naïve Bayes Algorithm. This research will also use the RStudio application. It is a computer programming language that allows users to program algorithms and use tools that have been developed through R by other users. R is a high-level programming language and is also an environment for data and graph analysis. Based on the experimental results, using a comparison of training data and test data of 20%: 80%, 40%: 60%, 60%: 40%, 80%: 20% and 90%:10%, the results of sentiment classification using the Naïve Bayes method are obtained. and using 10-fold cross validation obtained an average value of 85.00% accuracy and The decrease in machine learning performance occurs in the ratio of 80:20 or 1440 training data: 360 data testing, while the ratio of 20%:80% and 90%:10% has the same accuracy value, namely 85.41%.
Sentiment Analysis on Social Media X (Twitter) Against ChatGBT Using the K-Nearest Neighbors Algorithm Arwan Sulaeman, Asep; Danny, Muhtajuddin; Butsianto, Sufajar; Pratama, Suria
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4105

Abstract

This research aims to analyze the public's response to ChatGPT through data obtained from Twitter. Apart from that, it is also to understand whether people's responses tend to be positive or negative towards ChatGPT, as well as to test the performance of the K-Nearest Neighbors (KNN) method in classifying sentiment patterns in tweet data. The sentiment analysis method is carried out by dividing public responses into positive and negative categories. Next, the performance of the K-Nearest Neighbors (KNN) method was tested with varying k values ??to classify sentiment patterns in tweet data. This testing includes dataset division, vectorization of text data using TF-IDF, initialization and training of the KNN model, and evaluation of model performance using metrics such as precision, recall, and f1-score. The results of sentiment analysis show that the majority of people's responses to ChatGPT are positive (74.3%), while 25.7% of responses are negative. Performance testing of the KNN model shows that the highest accuracy of 88% is achieved when the k value is 5. Evaluation of model performance also shows satisfactory levels of precision, recall and f1-score. Based on the research results, it was concluded that sentiment analysis and classification using KNN were effective in understanding people's responses to ChatGPT
Prediction of Employee Assessments for Contract Extensions at PT Sagateknindo Sejati Using the Naïve Bayes Algorithm Naya, Candra; Siswandi, Arif; Butsianto, Sufajar; Febriyanti, Febriyanti
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4170

Abstract

Companies must be selective in conducting employee assessments in order to retain employees with the best performance. When assessing employee performance, it is seen from their perseverance and discipline. However, in reality, good employee performance sometimes gets bad reviews and even gets reprimanded by their superiors. This is caused by the employee assessment monitoring system used, namely only personal assessment without using an assessment system and the data collected is less than optimal. This research uses the Naive Bayes method to process data using a data mining algorithm to obtain predictions that can be used as additional references in making employee performance assessment decisions. Aims to predict employee assessments of contract extensions at PT Sagateknindo Sejati. This research is important because it helps in making more accurate decisions regarding employee contract extensions based on existing historical data. Naive Bayes is a data processing algorithm that is classified as a calculation that is easy to understand but its accuracy results are reliable. It is used because it is efficient in managing data with various attributes and is able to produce predictions based on the probability of each existing attribute. The data used in this research includes various variables, using the Rapidminer supporting application to test the accuracy of the system created. Testing was carried out by preparing 320 data and testing 50 randomly selected data. Test data will be analyzed using the Rapidminer supporting application. The test results produced an accuracy of 83.96%.
Eye Disease Detection and Classification Optimization Using EfficientNet-B5 with Emphasis on Data Augmentation and Fine-Tuning Anggi Muhammad Rifai; Muhammad Fatchan; Ahmad Turmudi Zy; Donny Maulana; Sufajar Butsianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6519

Abstract

Eye diseases such as glaucoma, cataract, and diabetic retinopathy pose significant global health challenges, underscoring the need for accurate and efficient diagnostic systems. This study employed the EfficientNet-B5 model to enhance the detection and classification of eye diseases by incorporating advanced data augmentation and fine-tuning techniques. The research utilizes the Ocular Disease Intelligent Recognition (ODIR) dataset, consisting of 4,217 fundus images categorized into four classes: normal, glaucoma, cataract, and diabetic retinopathy. The methodology comprises three phases: baseline model training, model training with data augmentation, and fine-tuning. The baseline model achieved an accuracy of 60.43%, which improved to 63.03% with data augmentation—an increase of 2.6 percentage points. Fine-tuning further elevated the accuracy to 93.23%, representing a notable improvement of 33.8 percentage points over the baseline. Model performance was evaluated using standard classification metrics including accuracy, precision, recall, and F1-score. These findings demonstrate the technical efficacy of combining augmentation and fine-tuning to enhance model generalization. The proposed approach offers a robust framework for developing dependable AI-driven diagnostic tools to support early detection and facilitate informed clinical decision-making.
Association Relationship Analysis in Finding Sales of Goods With Apriori Algorithm Fathurrahman, Humam; Sunge , Aswan S.; Butsianto, Sufajar
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4258

Abstract

Technology can be designed to help human life from all aspects ranging from agriculture, health science, industry and daily life. Toko Intan, a business engaged in the sale of basic and daily necessities. Every day, Toko Intan records every sales transaction in an archive stored in Microsoft Excel, containing data on goods sold every day. The purpose of this research is to find out what items are bought simultaneously by consumers to manage inventory, with the data mining method used in this research is the Association Rules method. Association Rules is one of the data mining techniques from the a priori algorithm which functions to find a combination pattern of an item. Tests carried out to process data in this study using the RapidMiner application, from tests carried out with the specified parameters, namely minimum support 30% and minimum confidence 65%, resulted in a lift ratio validation rule of 1.206. Personal Care Biscuits with 30.8% support and 90.9% confidence with a validation lift ratio of 1.206. Sales transaction data analysis can be applied well, and is able to generate a new association rule from the sales transaction dataset. With this research, it is hoped that it can provide information to the owner of the Intan store in providing the stock of goods needed by consumers and to find out the combination of item sets from the sales transaction dataset.
Co-Authors . Ermanto Abdul Halim Anshor Agung Nugroho Agus Suwarno Aguswin, Ahmad Ahmad Turmudi Zy Amali, Amali Ananda, Angga Thifal Ananto Tri Sasongko Andre Ardiansyah Andriani Andriani Andriani Andriani Anggi Muhammad Rifai Anisah Purnamasari Aprila Hardi, Resty Arief Nur Hidayat Arif Siswandi ARIF SUSILO Arif Tri Widiyatmoko Aris Iskianto Aris Iskianto2 Arwan Sulaeman, Asep Asep Muhidin Asep Muhidin Budi Rahardjo, Sugeng Candra Naya Dewi Sekar Arum Dian Riki Pangestu Dicky Winanda Santoso Donny Maulana Dwi Indra Prasetya Edi Tri Triwibowo Edi Tri Wibowo Edora Edora Edy Widodo Edy Widodo Eka Nur Arifin Eko Putra, Fibi Elkin Rilvani Endah Yaodah Kodratillah Ermanto Ermanto Fathurrahman, Humam Fauzi Ahmad Muda febriyanti febriyanti Fibi Eko Putra Herdiyan, Serly Humam Fathurrahman Ikhsan Romli Indradewa, Rhian Irfan, Yusuf Iwan Mulyana Kodratillah, Endah Yaodah M Ryan Bagus Valentin* Makmun Effendi, M. Mamat Casmat Maryani Manik Maulana, Futuh Muhamad Fatchan Muhammad Faisal Muhammad Fatchan Muhammad Fikri Fauzan muhidin, asep Muhtajuddin Danny Naya, Candra Nindi Tya Mayangwulan Nurhadi Surojudin Nurhali Saepudin Nurhali Saepudin Oktavianti, Risma Nadia Otib Subagja Pratama, Suria Puput Riyanti Purwanto Purwanto Putra, Irga Ramadhan Putri Riandani, Andini Rafli Maulana Raharjo, sugeng budi Ramadhan, Aldi Retno Fitri Astuti Rifa'i, Anggi Muhammad Rizal Ainun Yaqin Romli S. Sunge, Aswan Selviana, Vina Setyaningrum, Retno Purwani Setyaningrum Setyawan, Wisnu Sifa Fauziah Siswandi , Arif Siswandi, Arif Siti Rahayu Sulaeman, Asep Arwan Sunge , Aswan S. Supriyanto, Asep Suratman Suratman Suryadi Syahlan Sugiarto Tedi, Nanang Tri Ngudi Wiyatno Triwibowo, Edi Turmud Zy, Ahmad Wachid Hasyim, Wachid Wiyanto Wiyanto Wiyanto Yasmine Aprilia Fatimah Yolanda Alviana Zehan Prahardika