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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Yupa: Historical Studies Journal InComTech: Jurnal Telekomunikasi dan Komputer JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL INFORMATIKA Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Informatika dan Komputer Academic Journal of Computer Science Research (AJCSR) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Lentera Pengabdian SAINTEK Inaba of Community Services Journal
Donny Maulana
Universitas Pelita Bangsa
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 Public Health Social Sciences Other

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COMPARATIVE ANALYSIS OF CLASSIFICATION ALGORITHMS IN HANDLING IMBALANCED DATA WITH SMOTE OVERSAMPLING APPROACH Agung Nugroho; Wiyanto; Donny Maulana
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6956

Abstract

Most machine learning algorithms tend to yield optimal results when trained on datasets with balanced class proportions. However, their performance usually declines when applied to data with significant class imbalance. To address this issue, this study utilizes the Synthetic Minority Oversampling Technique (SMOTE) to improve class distribution before model training. Several classification algorithms were employed, including Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, and Random Forest. Experimental results reveal that the Random Forest model produced the highest accuracy (95.70%) and the best F1-score, demonstrating a well-balanced trade-off between precision and recall. In contrast, the Logistic Regression algorithm achieved the highest recall (74.20%), indicating better sensitivity in identifying positive instances despite a lower F1-score. These outcomes highlight the importance of choosing appropriate classification methods based on the specific evaluation goals whether prioritizing accuracy, recall, or overall model balance.
Analisis Sentimen Terhadap Program Makan Bergizi Gratis Menggunakan Metode Logistic Regression Ari Budi Indrawan; Donny Maulana; M. Zubair Abdurrohman
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3610

Abstract

This study aims to analyze public sentiment toward the Free Nutritious Meal Program (MBG), a policy implemented by the Indonesian government to improve the nutritional quality of students. The data used consist of 1,440 tweets collected through a scraping process from the X/Twitter platform. The data processing stages include preprocessing steps such as case folding, cleaning, tokenizing, stopword removal, and stemming using the Sastrawi library. Furthermore, the text data are transformed into numerical representations using the TF-IDF method and classified using the Logistic Regression algorithm. To enhance the model's performance, the SMOTE technique is applied to address data imbalance, along with GridSearchCV for parameter optimization. The results indicate that the Logistic Regression model achieves excellent performance, with an Accuracy of 98.96%, Precision of 99.14%, Recall of 98.10%, and an F1-Score of 98.61%. This study is expected to provide an objective overview of public perception and serve as a reference for policy evaluation and decision-making.Keywords: Sentiment Analysis; Free Nutritious Meal Program; Logistic Regression; Text mining; NLP.AbstrakPenelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap Program Makan Bergizi Gratis (MBG) yang merupakan kebijakan pemerintah Indonesia dalam meningkatkan kualitas gizi peserta didik. Data yang digunakan berupa 1.440 tweet yang diperoleh melalui proses scraping dari platform X/Twitter. Tahapan pengolahan data meliputi preprocessing yang terdiri dari case folding, cleaning, tokenizing, stopword removal, dan stemming menggunakan library Sastrawi. Selanjutnya, data teks diubah menjadi representasi numerik menggunakan metode TF-IDF dan diklasifikasikan menggunakan algoritma Logistic Regression. Untuk meningkatkan performa model, diterapkan teknik SMOTE dalam mengatasi ketidakseimbangan data serta GridSearchCV untuk optimasi parameter. Hasil penelitian menunjukkan bahwa model Logistic Regression memiliki kinerja yang sangat baik dengan akurasi sebesar 98,96%, Precision 99,14%, Recall 98,10%, dan F1-Score 98,61%. Penelitian ini diharapkan dapat memberikan gambaran objektif mengenai persepsi masyarakat serta menjadi bahan evaluasi dalam pengambilan kebijakan.Kata kunci: Analisis Sentimen; Makan Bergizi Gratis; Logistic Regression; Text mining; NLP.
Analisis Klasifikasi Risiko Penyakit Jantung Menggunakan Metode Random Forest Alfina Damayanti; Donny Maulana; M. Zubair Abdurrohman
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3609

Abstract

Heart disease is one of the leading causes of death worldwide, making early detection crucial to reduce the risk of complications and mortality. The advancement of machine learning technology enables fast and accurate analysis of medical data to support the diagnostic process. This study aims to develop a classification model for heart disease risk using the Random Forest algorithm. The dataset used is the Heart Disease Dataset from Kaggle, consisting of 1,025 patient records with 14 medical attributes, such as age, gender, blood pressure, cholesterol level, and maximum heart rate. The methodology applied is CRISP-DM, which includes Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Model Evaluation is conducted using a confusion matrix, cross-validation, and ROC-AUC. The results show that the Random Forest algorithm achieves a high Accuracy of 99.96% and a cross-validation score of 0.996. The variables chest pain, ca, and thalach are identified as the most influential factors in the prediction.Keywords: Heart Disease; Random Forest; Machine learning; Classification; CRISP-DM AbstrakPenyakit jantung merupakan salah satu penyebab utama kematian di dunia sehingga deteksi dini sangat penting untuk mengurangi risiko komplikasi dan kematian. Perkembangan teknologi machine learning memungkinkan analisis data medis secara cepat dan akurat dalam membantu proses diagnosis. Penelitian ini bertujuan membangun model klasifikasi risiko penyakit jantung menggunakan Algoritma Random Forest. Dataset yang digunakan adalah Heart Disease Dataset dari Kaggle yang terdiri dari 1025 data pasien dengan 14 atribut medis, seperti usia, jenis kelamin, tekanan darah, kadar kolesterol, dan detak jantung maksimum. Metode yang digunakan adalah CRISP-DM meliputi Data Understanding, Data Preparation, Modeling, Evaluation, dan Deployment. Evaluasi model dilakukan menggunakan confusion matrix, cross validation, dan ROC-AUC. Hasil penelitian menunjukkan bahwa Random Forest menghasilkan akurasi tinggi dengan nilai 99,96% serta cross validation sebesar 0,996. Variabel chest pain, ca, dan thalach menjadi faktor paling berpengaruh dalam prediksi.Kata kunci: Penyakit jantung; Random Forest; Machine learning; Klasifikasi; CRISP-DM.
TRAINING FOR MICRO, SMALL AND MEDIUM ENTERPRISES (MSMEs) BY MAKING WALL HANGING HANDICRAFTS FOR THE COMMUNITY OF SIMPANGAN CIKARANG UTARA VILLAGE, BEKASI REGENCY Miftakul Huda Huda; Daspar Daspar; Nani Hartati; Donny Maulana
Inaba of Community Services Journal Vol. 2 No. 1 (2023): Volume 2 No. 1, June 2023
Publisher : Universitas INABA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56956/inacos.v2i1.162

Abstract

Micro, Small0and Medium Enterprises0(MSMEs) are the business units that have an important role in the development and growth of the Indonesian economy. With the existence of the MSME sector,0unemployment due to the labor force that is not absorbed in the world of work i0 reduced. The purpose0of this community0service is to0find out the role0of digital marketing0training services for MSME0actors in Wangun0Harja Village.0In community0service in0Wangun Harja0Village, North Cikarang0District, there are04 methods used0including observation,0interviews, training,0and counseling.0Digital marketing0plays a role in0marketing MSME0products through0digital platforms. Among0the various0existing digital0platforms, researchers0chose Instagram0ads to market0MSME products in0Wangun Harja0village, North0Cikarang District,0Bekasi Regency.0Instagram Ads0is an advertising0platform on social0media. Instagram0Ads will allow0MSMEs to create0ads on0Instagram feeds and0Instagram stories.
Educational Transformation Through Holistic Literacy by Applying Digital Technology Innovation at SMPN 04 Cipayung Village – Bekasi Rismawati Rismawati; Miftakul Huda; Daspar Daspar; Donny Maulana
Inaba of Community Services Journal Vol. 3 No. 1 (2024): Volume 3 No. 1, June 2024
Publisher : Universitas INABA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56956/inacos.v3i1.287

Abstract

Globalization era gives quite an impact wide in various aspect life, incl demands in implementation of education. One of challenge real the is that education should capably produce resource humans who have competence, therefore That Transformation education through literacy holistic capable create development, updates and adjustments paradigm educator with demands era educational human resources are superior, creative, and innovative, among others with apply innovation digital technology as means for expand range transformation education culture. Use of online platforms, social media, and applications education help connect public with information, inspire participation active, and open door for learning collaborative. The impact covers enhancement participation in discussion, training, and dissemination of information related environment and literacy. The purpose of devotion to the public is to apply the role of education through educational transformation through literacy holistic with apply innovation digital technology. Methods used are observation, interviews, training, and counseling. Transformation education through holistic literacy with apply innovation digital technology shows that ability 21st century that is needed in the world of education are skills and learning innovate, information media and skills technology.
Implementasi Sistem Informasi Web Integrasi Chatbot Knowledge-Based Menggunakan Metode Agile Sprint Studi Kasus “PT. Khasanah Agung Jaya” Syifa Aurellia Rahma; Donny Maulana; Edora Edora
JURNAL INFORMATIKA Vol 15, No 1 (2026): Jurnal Informatika
Publisher : Informatics Engineering Department, Dayanu Ikhsanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55340/jiu.v15i1.2697

Abstract

PT. Khasanah Agung Jaya menghadapi kendala dalam penyampaian informasi layanan dan pengelolaan dokumen yang masih dilakukan secara manual. Penelitian ini bertujuan mengembangkan sistem informasi berbasis web yang terintegrasi dengan chatbot knowledge-based untuk meningkatkan efisiensi layanan informasi dan administrasi dokumen. Pengembangan sistem dilakukan menggunakan metode Agile Sprint yang meliputi tahap perencanaan, perancangan, pengembangan, pengujian, dan evaluasi. Hasil pengujian menunjukkan seluruh fungsi sistem berjalan dengan baik melalui Black Box Testing dengan tingkat keberhasilan 100%. Selain itu, chatbot memperoleh akurasi sebesar 92% berdasarkan kesesuaian respons terhadap basis pengetahuan yang telah ditentukan, sedangkan ketahanan terhadap kesalahan pengetikan mencapai 74%, yang menunjukkan masih terdapat ruang peningkatan dalam pemrosesan variasi bahasa pengguna. Hasil penelitian menunjukkan bahwa integrasi chatbot knowledge-based dan manajemen dokumen digital mampu mendukung otomatisasi layanan pada sektor properti.
Co-Authors Agung Nugroho Agung Nugroho Agus Suwarno, Agus Ahmad Turmudi Zy Ahmad Turmuzdy ZY Alfatan Dzulatkha2 Alfina Damayanti Andri Firmansyah Anggi Muhammad Rifai Ari Budi Indrawan Asep Saepudin Butsianto, Sufajar Daspar Daspar Edora Edora Edri Fauzan Emi Lia Nurjanah Emilda Yulian Sari Gatot Tri Pranoto Hariyono Hariyono Hasim Budi Jatmiko Ikhsanudin ikhsanudin Indah Wahyu Puji Utami, Indah Wahyu Irfan Afriantoro Ismamudi, Ismamudi Linda Wahyu Setyoningsih Lutfi Adji Ardiansyah M. Zubair Abdurrohman Maryatul Kiptiyah Maulana Sofyan Miftakul Huda Muhammad Fatchan Muhtajuddin Danny Nanang Tedi Nanang Tedi Kurniadi Nani Hartati Nawangsih, Ismasari Novita Sari Raden Rangga Bramantya Putra Rensi Suryanti Rezayadi Yahya Riady, Sasmitoh Rahmad Rismawati Rismawati Rudi Rahmanto2 Shinta Mawarni Siti Fadillah Rauf Slamet Nur Ihsan Suherman Suherman Syifa Aurellia Rahma Taufik Hidayat Ucok Darmanto Soer Vivi Mustikaningtyas Wildan Trio Munawarudin Wiyanto Wiyanto - Yoga Religia Yusup Solikhun