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All Journal BERKALA FISIKA JURNAL SISTEM INFORMASI BISNIS YOUNGSTER PHYSICS JOURNAL Jurnal Sains dan Teknologi Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Physics and Its Applications Journal of Information Systems and Informatics Jurnal Fisika Indonesia JINAV: Journal of Information and Visualization Jurnal Algoritma Journal of Emerging Science and Engineering Scientific Journal of Informatics
Catur Edi Widodo
Departemen Fisika, Fakultas Sains Dan Matematika, Universitas Diponegoro, Semarang
Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Physics

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Optimization of Coronary Heart Disease Risk Prediction Using Extreme Learning Machine Algorithm (Case Study: Patients of Dr. Soeselo Hospital) Iswanti, Arie; Isnanto, R. Rizal; Widodo, Catur Edi
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24746

Abstract

Purpose: Coronary heart disease (CHD) is the leading cause of death globally, with 17.8 million deaths reported by the WHO in 2021. Early detection remains a major challenge due to low public awareness and dependence on manual diagnostic procedures. These limitations necessitate the development of automated and accurate predictive models. This study aims to construct a CHD risk prediction model using the Extreme Learning Machine (ELM) algorithm. The research addresses a critical limitation in existing models, namely, poor performance on minority classes (CHD stages 2–4), caused by data imbalance. To overcome this, oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are applied. The objective is to improve classification performance, particularly in high-risk categories, and to enhance the model’s generalisation capability for real-world implementation. Methods: This research implements the Extreme Learning Machine (ELM) algorithm to achieve optimal prediction results. The data used in this study as the initial database of patients consists of gender, age, height, weight, whether they have diabetes or not, the number of cigarettes consumed daily, and blood pressure. The data will be the main component in building the heart disease prediction system. The prediction classes are: no heart disease, stage 1 heart disease, stage 2 heart disease, stage 3 heart disease, and stage 4 heart disease. The total number of dataset are 521 data points, with 70% of the training data amounting to 364 patients, and 30% of the test data amounting to 157 patients. The data collection process uses patient data from RSUD Dr. Soeselo, Tegal Regency, Central Java, for the years 2023 and 2024. Result: The research successfully developed and evaluated an Extreme Learning Machine (ELM) algorithm for Coronary Heart Disease (CHD) risk prediction using patient data from Dr. Soeselo Hospital. The model achieved an overall accuracy of 82% on the dataset of 157 patients, demonstrating a promising capability for automated risk assessment. Novelty: This predictive model can be utilised in the medical field to facilitate the early detection of heart disease or other risks. This model will soon be introduced in hospitals in the Tegal Regency and City area, Central Java.
Analisis Sentimen Berbasis Aspek Pada Aplikasi Elektronik Survei Kepuasan Masyarakat (E-SKM) Jawa Tengah Menggunakan Indobert Labib Mustofa, Refo; Labib Mustofa, Tarno; Edi Widodo, Catur
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 4: Agustus 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.124

Abstract

Perkembangan tentang Natural Language Processing (NLP) semakin berkembang dengan pesat, salah satunya yaitu dalam bidang analisis sentimen. Dalam dunia bisnis, analisis sentimen sangat diperlukan untuk mengetahui dan memahami persepsi pelanggan terhadap produk yang telah didapatkan dari perusahaan. Hal yang sama juga berlaku pada sektor pemerintahan. Pemerintah sebagai penyelenggara pelayanan publik harus dapat mengetahui persepsi dari pengguna layanan terhadap penyelenggaraan pelayanan publik tersebut sebagai bahan perbaikan kualitas layanan. Aplikasi E-SKM merupakan aplikasi milik Pemerintah Provinsi Jawa Tengah yang saat ini hanya mengolah nilai survei layanan meliputi sembilan aspek pertanyaan, sedangkan data saran/masukan pada aplikasi ini belum dimanfaatkan lebih lanjut. Pada penelitian ini, dilakukan analisis sentimen pada data saran/masukan tersebut untuk menggali informasi tambahan yang dapat meningkatkan pemahaman pemerintah terhadap kepuasan pengguna layanan. Metode yang diusulkan yaitu menggunakan pendekatan analisis sentimen berbasis aspek menggunakan model IndoBERT. Pendekatan berbasis aspek ditujukan agar dapat diketahui aspek apa saja yang paling banyak dibicarakan oleh pengguna layanan, terutama yang berhubungan dengan sembilan aspek pertanyaan tersebut. Pada penelitian ini juga digunakan kamus leksikon sebagai pelabelan data, kemudian pendekatan berbasis aturan (rule-based) digunakan dalam proses klasifikasi aspek yang berkaitan dengan sembilan aspek pertanyaan. Selain itu, penelitian ini bertujuan untuk mengukur kemampuan model IndoBERT dalam proses klasifikasi sentimen dengan beberapa skenario yang berbeda. Dari hasil analisis, model evaluasi IndoBERT berjalan dengan baik. Hal ini dilihat dari nilai rata-rata parameter evaluasi seperti akurasi, precision, recall, dan f1-score mencapai 95%. Penerapan model ini memiliki kontribusi pada data aplikasi E-SKM untuk mendapatkan informasi sentimen dan aspek pada data pelayanan publik di pemerintahan yang dapat digunakan sebagai bahan pengambilan keputusan pada level manajemen kebijakan.   Abstract The field of Natural Language Processing (NLP) is rapidly advancing, particularly in sentiment analysis. In the business world, sentiment analysis is essential for understanding customer perceptions of products they have received from a company. The same applies to the government sector, where it is crucial for public service providers to gain insight into user perceptions of public services as a basis for service improvement. The E-SKM application, owned by the Central Java provincial government, currently processes only service survey scores covering nine question aspects, while suggestions/feedback data from this application have not yet been fully utilized. In this study, sentiment analysis was conducted on the suggestion/feedback data to extract additional insights that could improve understanding of user satisfaction. The proposed method involves an aspect-based sentiment analysis approach using the IndoBERT model. This aspect-based approach aims to identify the aspects most frequently mentioned by service users, particularly those related to the nine survey aspects. A lexicon-based approach was used for data labeling, followed by a rule-based approach for classifying aspects associated with the nine questions. Additionally, this study aims to assess the performance of the IndoBERT model in sentiment classification across several scenarios. Evaluation results indicate that IndoBERT performs well, with average metrics such as accuracy, precision, recall, and F1-score reaching 95%. The implementation of this model contributes to the E-SKM application data by providing sentiment and aspect information on public service data within the government, which can be used as a basis for decision-making at the policy management level.
Application of Life Simulation Games in Teaching Network Security and Cryptography Taufani, Agusta Rakhmat; Soeprobowati, Tri Retnaningsih; Widodo, Catur Edi
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1161

Abstract

Information security-related mathematical methods are used in the science of cryptography. A collection of methods that offer information security, cryptography is more than just a means of concealing messages. Using only presentation slides or video links at each meeting, the interaction between lecturers and students via SIPEJAR e-learning hinders the Network Security and Cryptography learning process at the State University of Malang (UM) Information Engineering (IT) Undergraduate Study Program. To help students learn more about the area of encoding using SIPEJAR, a game that explicitly explains cryptography was created using these several challenges as the background. The creation of a cryptographic life simulation game is intended to serve as a teaching and learning aid for lecturers and students. Students are expected to better understand related material in a learning atmosphere that is new, more interesting, opens the horizons of the mind, and is more investigative. After going through the equivalence partitioning testing process, in general this system produces a total percentage of 100% in system item test success in the testing process of the 6 item tests carried out and a respondent satisfaction percentage of 84.3%. Thus, the system is running according to the prototype design.
Global Research Trends and Map on Machine Learning Applications in Stunting Detection in Vulnerable Populations: A Bibliometric Analysis Bachri, Otong Saeful; Widodo, Catur Edi; Nurhayati, Oky Dwi
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1248

Abstract

Stunting and malnutrition continue to be significant public health challenges, particularly in low-income and rural populations. With the growing reliance on data-driven strategies in public health, machine learning (ML) has emerged as a promising tool for identifying, classifying, and predicting conditions related to undernutrition. This study presents a bibliometric analysis of global research from 2019 to 2025, focusing on the application of ML techniques—such as clustering, support vector machines (SVM), and random forest—in addressing malnutrition and stunting. A total of 417 Scopus-indexed publications were analyzed using Biblioshiny (R) to assess research trends, key themes, influential authors, prominent journals, and thematic evolution. The analysis reveals a consistent growth rate of 10.72% in publications, with notable contributions from China and other low- and middle-income countries. Keyword mapping highlights that “machine learning,” “spatial analysis,” and “stunting” are central to the research, although they remain areas for further development. Thematic evolution indicates a shift towards more integrated, context-aware approaches, with a growing focus on built environments and vulnerable populations. The study concludes that while ML holds significant promise for advancing decision-making in child health and nutrition, its impact will depend on continued methodological refinement and effective implementation within public health systems.
Topic Modelling Latent Dirichlet Allocation untuk Klasifikasi Komentar pada Layanan Streaming Platform Royani, Noorhanida; Widodo, Catur Edi; Warsito, Budi
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 3 (2023): Oktober
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i3.68492

Abstract

Seiring dengan berkembangnya teknologi, memunculkan banyak platform online untuk streaming film. Streaming platform banyak digunakan masyarakat seperti netflix, disney+, hbo go, we tv, vidio. Banyaknya perbandingan antar streaming platform menjadi perbincangan dimedia sosial yaitu twitter. Opini yang disampaikan pengguna streaming platform berisi komentar positif dan komentar negatif yang mempengaruhi pengguna lainnya yang ingin menonton film. Penelitian ini dilakukan untuk mengkaji perbandingan antara komentar positif dan komentar negatif pengguna streaming platform pada media sosial Twitter. Metode Latent dirichlet allocation dapat digunakan sebagai topic modelling dan Support Vector Machine untuk klasifikasi. Pada tahapan pengambilan data dengan menggunakan tools framework scrapy dengan python, data diambil sebanyak 5.000 dan dilakukan preprocessing text. Metode LDA dapat mempresentasikan topik dan dokumen serta klasifikasi menggunakan Support Vector Machine (SVM) mendapatkan hasil komentar positif lebih banyak dari pada komentar negatif. Hasil evaluasi preforma didapatkan nilai akurasi 0,88, recall 0,88, F1score 0,87, precision 0,88. Topic Modelling Latent Dirichlet Allocation (LDA) untuk Klasifikasi Komentar pada Layanan Streaming Platform dengan menggunakan 5,000 data diambil dari sosial media yaitu twitter yang terbagi menjadi komentar positif dan komentar negatif. Hasil ini dipengaruhi dari jumlah komentar positif yang lebih dominan dari pada komentar negatif. Implikasi dari penelitian ini adalah pentingnya memperhatikan keseimbangan data dalam melakukan klasifikasi komentar pada platform streaming agar hasil prediksi klasifikasi dapat lebih akurat.
Co-Authors Agusta Rakhmat Taufani Aprilia Ayu Andarinny Arif Setiawan Budi Warsito Choirul Anam AM Diponegoro Didik Supriyadi, Didik Eko Sediyono Fatkhur Rohman Figur Humani Fitra Nur Asri, Muh H Hadiyanto Hadyan Arifianto Husen Nasrullah Armin I Gusti Bagus Wiksuana Irwan Agus Saputro Isnain Gunadi Iswanti, Arie Jatmiko Endro Suseno K. Sofjan Firdausi Kusworo Adi Kuworo Adi Labib Mustofa, Refo Labib Mustofa, Tarno Muchammad Azam, Muchammad Mustafid Mustafid Oky Dwi Nurhayati Otong Saeful Bachri R Rizal Isnanto R. Rizal Isnanto Rachmat Gernowo Rahmatika, Yuni Ramanda, Febri Retno Hapsari, Angga Ayu Rizky Ayomi Syifa Romadhon, Zainur Royani, Noorhanida Saitem Saitem Salsabila Naqiyah Sri Mulyani Tri Retnaningsih Soeprobowati Tutur Urip Ucky Pradestha Novettralita Wahyu Adi Wibowo Yulaikha Maratullatifah Zainal Bachrudin