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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 Infotech: Journal of Technology Information 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 VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Pelita Pengabdian SAINTEK Inaba of Community Services Journal Cahaya Pengabdian
Donny Maulana
Universitas Pelita Bangsa
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy 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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Pengembangan Sistem Pakar Diagnosis Penyakit Dengan Metode Certainty Factor Untuk Mendukung Keputusan Medis Cepat dan Tepat Gatot Tri Pranoto; Donny Maulana; Ismasari Nawangsih
Academic Journal of Computer Science Research Vol 5, No 2 (2023): Academic Journal of Computer Science Research (AJCSR)
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/ajcsr.v5i2.3800

Abstract

Diagnosis penyakit merupakan salah satu proses yang sangat menentukan dalam penangan kesehatan seorang pasien. Hasil diagnosis akan menentukan riwayat penyakit seorang pasien. Bahkan, hasil diagnosis tersebut akan menentukan langkah perawatan yang harus diberikan kepada pasien sebagai penanganan. Namun, permasalahan yang ada saat ini adalah proses diagnosis masih dilakukan secara manual dan membutuhkan waktu yang lama serta masih tergantung kepada kemampuan seorang dokter. Masalah sekarang ini sebuah penyakit cepat bermutasi dan berkembang sehingga langkah penanganan harus selalu diperabaharui. Sementara itu, jumlah dokter dan tenaga kesehatan yang ada lebih sedikit jika dibandingkan dengan jumlah pasien yang ada. Akibatnya, dokter akan kewalahan dan bisa saja salah diagnosis hingga penanganan. Diperlukan suatu model diagnosis yang cepat dan tepat pada rumah sakit, puskesmas dan klinik untuk mengatasi hal tersebut. Penelitian ini mengembangkan sistem pakar dengan menerapkan metode certainty factor yang dapat membantu dokter dalam melakukan diagnosis penyakit. Untuk pengembagan model diagnosis, digunakan model waterfall dan memanfaatkan tool UML dan Bahasa PHP. Penelitian ini menghasilkan model diagnosis metode certainty factor dengan tingkat keyakinan mencapai 56,7% atau dengan interpretasi Sangat Mungkin. Pengujian kehandalan dan kebergunaan aplikasi dalam penelitian ini menggunakan model Software Usability Scale (SUS) dengan hasil 82 yang artinya sistem pakar memberikan rekomendasi secara cepat dan tepat. Model Diagnosis dalam penelitian ini dapat diterima dengan baik oleh user berdasarkan pengujian Technology Acceptance Method (TAM) dengan nilai rata-rata 83,04%.
Penerapan Data Mining Untuk Klasifikasi Kepuasan Pelanggan Transportasi Online (Ojek Online) Menggunakan Algoritma C.4.5 Suherman Suherman; Donny Maulana; Vivi Mustikaningtyas
Prosiding Sains dan Teknologi Vol. 1 No. 1 (2022): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 1 - Juli 2022
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/SAINTEK0101.165175

Abstract

Human activities to meet their daily needs and needs, both at work, or just for a walk. So, this needs to be supported by adequate transportation. With the development of technology today there are applications that introduce motorcycle taxi booking services using technology and use service standards. In Indonesia there are many motorbikes, which also function as general vehicles, namely transporting people / goods. Currently there are many online transportation service providers (online motorcycle taxi) known as Go-Jek, Grab, and Uber. Customer satisfaction input attributes in this study include price, facilities, service and loyalty. Data mining is a series of processes to explore added value in the form of information that has not been known manually from a database. In this study it is expected to help the online transportation services in increasing customer satisfaction. Based on the results of the classification using C4.5 algorithm shows that the accuracy reached 75.33%, which shows that the C4.5 algorithm is suitable for measuring the level of satisfaction of online transportation. Keywords: Satisfaction of online transportation, Data Mining, Algoritma C.4.5
Sistem Monitoring Suhu Dan Kelembapan Udara Berbasis Internet Of Things Pada Ruang Kerja Proses Injection Molding Donny Maulana; Ikhsanudin ikhsanudin
Prosiding Sains dan Teknologi Vol. 1 No. 1 (2022): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 1 - Juli 2022
Publisher : DPPM Universitas Pelita Bangsa

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

Abstract

The development of technology is currently growing very rapidly and can be felt in the industrial world and in society, one of which is the application of the Internet of Things (IoT) in the work environment. The work environment is all things or elements that can affect directly or indirectly the organization or company that will have a good or bad impact on employee performance and job satisfaction. The purpose of this research on temperature and humidity monitoring systems is to monitor the stability of temperature and humidity in the injection molding process workspace, where room temperature is related to the engine cooling temperature and affects the results of production. This temperature and humidity monitoring system is designed using the NodeMCU ESP8266 module and the DHT11 sensor using the Arduino IDE program C language and the Thingspeak web server. In this research, the methodology used is the prototype methodology. The prototype model is a technique to collect certain information about the user's information needs quickly. As a result, manual thermometers can be replaced with Internet of Things-based temperature sensors. The temperature and humidity monitoring system can be applied in the injection molding process workspace so that it can speed up the response by the facility in the event of an abnormality in room temperature. Keywords: Internet of Things, Thingspeak, NodeMCU ESP8266, DHT11 Sensor
Pengembangan Sistem Pembelajaran Siswa Berbasis Metaverse pada TK Islam Pelita Insan Donny Maulana; Ismamudi, Ismamudi; Wiyanto, Wiyanto
Lentera Pengabdian Vol. 1 No. 01 (2023): Januari 2023
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/lp.v1i01.21

Abstract

TK Islam Pelita Insan adalah salah satu sekolah swasta berbasis Islam Terpadu yang terletak di daerah lemah Kabupaten Bekasi. Sebagai sekolah yang mulai bergerak maju, maka TK Islam Pelita Insan berusaha meningkatkan mutu dan kualitas sekolahnya dengan pemanfaatan teknologi khususnya pengembangan pada sistem pembelajaran siswanya. Sistem pembelajaran sekolah saat ini masih menggunakan gambar kertas yang diprint berdasarkan gambar dan ditempel dikelasnya. Sistem yang berjalan saat ini sebenarnya sudah baik, tetapi masih ada kelemahan yang dirasakan, diantaranya gambar print out kertas ini mudah hilang dan tercecer. Kelemahan lain yaitu adanya pemborosan kertas dan waktu unuk mencetak pembelajaran siswa per guru. Belum lagi permasalahan yang disebabkan oleh gambar yang hilang pada setiap periode semester saat masa pembagian rapor masih manual oleh guru tersebut untuk diserahkan ke wali kelas. Kondisi ini tentunya menyebabkan pekerjaan dinilai tidak efektif dan efisien bagi guru. Penelitian ini membuat sebuah sistem informasi pembelajaran siswa berbasis web menggunakan metaverse yang dikembangan oleh mozilla hub dengan tampilan 3 Dimensi serta menggunakan tampilan web. Dari hasil penelitian dapat disimpulkan bahwa dengan sistem ini maka model pengajaran harian siswa menggunakan print out untuk pengajaran 1 semester sudah tidak digunakan lagi, digantikan dengan model pengajaran dengan cara mengakses internet dengan mengunjungi web mozilla hub. Dengan sistem ini maka tidak ada lagi guru jam kedua dan seterusnya kebingungan mencari pengajaran apabila guru jam pertama lupa tidak menaruh pengajaran tersebut selepas mengajar di kantor serta memudahkan pihak sekolah untuk merekap pengajaran siswa pada setiap periode semester untuk diberikan kepada wali kelasnya.
Sistem Pendukung Keputusan Pemilihan Jasa Pengiriman Terbaik Menggunakan Metode Maut (Multi-Attribute Utility Theory) Wildan Trio Munawarudin; Donny Maulana; Ucok Darmanto Soer; Rensi Suryanti; Edri Fauzan
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i2.6037

Abstract

Karena kemajuan teknologi, pelaku e-commerce kini dapat menggunakan media sosial untuk menjual produk mereka. Toko online seperti Shopee, Bukalapak, dan Lazada memberikan bantuan yang lebih besar kepada masyarakat. Layanan pengiriman dibutuhkan jika orang ingin menjual barang mereka di toko online. Penelitian ini bertujuan untuk membangun Sistem Pendukung Keputusan (SPK) yang dapat membantu konsumen dalam proses pengambilan keputusan terkait layanan pengiriman dengan menggunakan metode Multi-Attribute Utility Theory (MAUT). MAUT digunakan untuk menyelesaikan masalah pengambilan keputusan yang melibatkan beberapa kriteria dengan mengumpulkan informasi dan memberikan bobot pada setiap kriteria berdasarkan tingkat kepentingannya. Hasil akhirnya berupa nilai numerik dalam skala 0–1. Dari hasil penelitian, ditemukan bahwa alternatif A1 (JNE) merupakan layanan pengiriman terbaik, diikuti oleh J&T Express, Pos Indonesia, SiCepat, dan AnterAja. Kriteria ketepatan waktu, keamanan pengiriman, biaya pengiriman, layanan pelanggan, cakupan layanan, dan kemudahan penggunaan sangat penting bagi pengguna. Penelitian ini menyimpulkan bahwa SPK yang dibangun mampu memberikan solusi efektif dalam pemilihan layanan pengiriman. Penerapan metode MAUT membantu mengurangi subjektivitas dan memungkinkan penilaian yang lebih objektif serta sistematis, sehingga membantu konsumen membuat keputusan yang lebih informasional dan rasional.
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.
Redefine Our Nationalism? Critical Reflection on Indonesian Nationalism as a "Joint Project": Case Study of Post-1998 Racial Communal Conflict Donny Maulana; Hariyono, Hariyono; Utami, Indah Wahyu Puji
Yupa: Historical Studies Journal Vol. 8 No. 4 (2024)
Publisher : Program Studi Pendidikan Sejarah FKIP Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/yupa.v8i4.4102

Abstract

The discourse on Indonesian Nationalism will never end. As the basis of the state, nationalism needs to continue to be reflected to remain relevant to current developments. The phenomenon of narrowing the meaning of Indonesian Nationalism and stagnation in the mainstream narrative which leads to the fossilisation of meaning is also an important and urgent topic to study. The big question is whether Indonesian Nationalism is still on rails and corridors that are relevant to the current condition of the nation. This article will specifically dissect in a deconstructive manner the paradigm of Indonesian Nationalism as a "joint project" as a form of response to stagnation and narrow mainstream narratives about nationalism. This paper also analyzes it in a historical framework, specifically taking a case study of racial communal conflict after the 1998 Reformation using the analytical knife of Benedict Anderson's Imagined Communities. There are three sub-discussions which will explain the background conditions and challenges of Indonesian Nationalism, the deconstruction of the paradigm of Indonesian Nationalism and how nationalism should be addressed as a "joint project" of a nation.
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.
Co-Authors Agung Nugroho Agung Nugroho Agus Suwarno, Agus Ahmad Turmudi Zy Ahmad Turmuzdy ZY Alfatan Dzulatkha2 Alfina Damayanti Amali Amali Amali Andri Firmansyah Anggi Muhammad Rifai Anggi Muhammad Rifa’i Ari Budi Indrawan Asep Saepudin Butsianto, Sufajar Daspar Daspar Edora Edora Edri Fauzan Eko Budiarto Emi Lia Nurjanah Emilda Yulian Sari Gatot Tri Pranoto Hariyono Hariyono Hasim Budi Jatmiko Ihsan Hadimulya Ikhsanudin ikhsanudin Indah Wahyu Puji Utami, Indah Wahyu Irfan Afriantoro Isarianto Isarianto Ismamudi Ismamudi Ismamudi, Ismamudi Linda Wahyu Setyoningsih Lutfi Adji Ardiansyah M. Zubair Abdurrohman Maha Putra Maryatul Kiptiyah Maulana Sofyan Miftakul Huda Muhammad Fatchan Muhtajuddin Danny Nanang Tedi Nanang Tedi Kurniadi Nani Hartati Nawangsih, Ismasari Novita Sari Nurhidayanti, Nisa Pupung Purnamasari Raden Rangga Bramantya Putra Rensi Suryanti Rezayadi Yahya Riady, Sasmitoh Rahmad Rismawati Rismawati Rudi Rahmanto2 Sesri Sellina Shinta Mawarni Siti Fadillah Rauf Slamet Nur Ihsan Suherman Suherman Sunita Dasman Suriyanti Syifa Aurellia Rahma Taufik Hidayat Ucok Darmanto Soer Vivi Mustikaningtyas Wildan Trio Munawarudin Wiyanto Wiyanto - Yoga Religia Yusup Solikhun