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All Journal Jurnal EECCIS Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal Faktor Exacta INOVTEK Polbeng - Seri Informatika Jurnal Elektronika dan Telekomunikasi Jurnal Sisfokom (Sistem Informasi dan Komputer) KACANEGARA Jurnal Pengabdian pada Masyarakat EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Science, Engineering, Technology, and Education Jurnal Pengabdian Masyarakat Multidisiplin Jifosi Jurnal Syntax Transformation SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal PkM (Pengabdian kepada Masyarakat) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Kresna: Jurnal Riset dan Pengabdian Masyarakat Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal INFOTEL Jurnal Ticom: Technology of Information and Communication INOVTEK Polbeng - Seri Informatika
Hari Soetanto
Universitas Budi Luhur
Aerospace Engineering Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture 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 Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Anomaly Detection in VPN (Virtual Private Network) Access Using Machine Learning Algorithms Prasetiyo, Ari Budi; Soetanto, Hari; Wibowo, Danang Harito
Jurnal Syntax Transformation Vol 6 No 9 (2025): Jurnal Syntax Transformation
Publisher : CV. Syntax Corporation Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/jst.v6i9.1109

Abstract

This research discusses the physical and spatial characteristics of the Ahmad Djuhara Creative Space which is located in the Cirebon City State Building, West Java. In this research, the author uses a qualitative-descriptive research method by studying building characteristics with the architectural characteristics theory by N. John Habraken academically. The Ahmad Djuhara Creative Space building functions as a typical arts and culture workshop from Cirebon. From the study that has been carried out, it is concluded that what forms the physical and spatial character of the Ahmad Djuhara Creative Space building is the roof which has a repeating triangular shape, where the shape of the roof looks different from similar buildings.
Comparing CNN and GRU for Gold Price Prediction Using Deep Learning Dwi Agung Yanumatrajaya; Asmarayani , Ilham Fikri Dwi; Soetanto, Hari
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2406

Abstract

This research proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to predict gold prices. The motivation stems from the volatile and complex nature of the gold market, heavily influenced by macroeconomic indicators such as the exchange rate (IDR/USD), Bank Indonesia (BI) interest rate, and inflation. In the hybrid architecture, the CNN serves as a feature extractor to identify nonlinear patterns in historical and economic data. At the same time, the GRU captures temporal dependencies, enabling the model to learn both short-term and long-term dynamics. The dataset comprises daily gold prices from January 2020 to August 2024, enriched with macroeconomic indicators to improve predictive relevance. Experimental results show rapid convergence of training and validation losses within 12 epochs. Model evaluation using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) demonstrates high predictive accuracy, with a MAPE of 1.136%. A comparative analysis with standalone CNN and GRU models reveals that the hybrid CNN–GRU architecture consistently outperforms both in terms of accuracy and prediction stability. This study contributes to financial forecasting by providing a robust, data-driven predictive tool that can support timely investment decisions in volatile market conditions.
Penerapan Metode Certainty Factor dan Interpolasi Untuk Diagnosa Penyakit Kolik Abdomen Pada Rumah Sakit Qadr Tangerang Suryadewiansyah, Muhammad Kamil; Soetanto, Hari
Jurnal Ticom: Technology of Information and Communication Vol 12 No 1 (2023): Jurnal Ticom-September 2023
Publisher : Asosiasi Pendidikan Tinggi Informatika dan Komputer Provinsi DKI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70309/ticom.v12i1.105

Abstract

Kolik abdomen adalah nyeri pada perut yang disebabkan karena terjadi pembesaran, sumbatan atau peradangan pada organ tubuh. Sering kali nyeri pada perut dianggap sebagai penyakit maag biasa. Hal ini menyebabkan terdapat 259 juta kasus usus buntu pada pria yang tidak terdiagnosa. Sebagian besar penyakit kolik abdomen harus ditangani lewat jalur operasi, karena kolik abdomen terdiri dari beberapa penyakit yang memiliki gejala yang hampir mirip. Beberapa kali saat dilakukan operasi ditemui penyakit jenis lain pada pasien, sehingga menambah waktu dan tenaga dokter. Urgensi penelitian ini dapat berdampak pada konsentrasi dan kinerja dokter dalam melakukan tindakan operasi, serta meningkatkan resiko komplikasi pada pasien yang berujung pada kematian. Solusi penelitian ini menerapkan sebuah sistem pakar berbasis model aplikasi web. Tahapan penelitian yang diusulkan, meliputi; tahap pengumpulan data, processing data, metode interpolasi, metode certainty factor, dan pengujian. Kontribusi penelitian ini kombinasi metode certainty factor dan interpolasi untuk diagnosis penyakit kolik abdomen menggunakan 29 gejala dan 14 penyakit serta menggunakan bobot nilai keyakinan user yang disesuaikan dengan form konsultasi user. Metode interpolasi diperuntukan untuk hasil lab (Laboratorium) dan metode certainty factor diperuntukan untuk anamnesa dan pemeriksaan fisik. Hasil penelitian menunjukkan akurasi sebesar 96%, terdapat 96 pasien yang terdiagnosa dengan tepat oleh sistem sesuai dengan data aslinya yaitu 100 data uji pasien operasi rumah sakit Qadr Tangerang.
Peningkatan kompetensi algoritma dan pemrograman C/C++ bagi siswa dan siswi SMK YADIKA 4 Painem, Painem; Soetanto, Hari; Kristanto, Dwi; Solichin, Achmad; Rusdah, Rusdah
KACANEGARA Jurnal Pengabdian pada Masyarakat Vol 6, No 4 (2023): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/kacanegara.v6i4.1689

Abstract

Salah satu bentuk tridharma perguruan tinggi adalah pengabdian kepada masyarakat. Selain menyelenggarakan pendidikan dan penelitian, perguruan tinggi juga memiliki tanggung jawab untuk memberikan kontribusi yang nyata bagi masyarakat di sekitar mereka. Pelatihan pemrograman bahasa C pada SMK Yadika 4 merupakan salah satu kontribusi nyata perguruan tinggi bagi masyarakat sekitar. Pelatihan pemrograman C/C++ dan kompetensi algoritma menjadi hal yang penting bagi siswa/siswi SMK Yadika 4. Hal ini bertujuan untuk meningkatkan kualitas pendidikan dan kesiapan siswa/siswi dalam memasuki dunia kerja yang membutuhkan kemampuan pemrogramanPelatihan pemrograman C/C++ dan kompetensi algoritma menjadi hal yang penting bagi siswa/siswi SMK Yadika 4 serta membekali siswa/siswi dengan pengetahuan dan keterampilan dasar pemrograman C/C++ sehingga mereka dapat mengembangkan aplikasi sederhana. Selain itu, pelatihan ini akan meningkatkan kompetensi algoritma siswa/siswi dalam memecahkan masalah dan merancang solusi yang tepat menggunakan algoritma yang efektif. Hal ini bertujuan untuk meningkatkan kualitas pendidikan dan kesiapan siswa/siswi dalam memasuki dunia kerja yang membutuhkan kemampuan pemrograman. Dalam pelatihan ini, siswa/siswi akan diberikan pemahaman dan latihan tentang konsep dasar pemrograman C/C++ dan kompetensi algoritma. Pelatihan ini akan meliputi pembelajaran teori dan juga praktek pengembangan program, di mana siswa/siswi akan belajar mengenai sintaks dasar, variabel, tipe data, operator, penggunaan loop dan kondisi, fungsi, dan lain sebagainya. Dengan meningkatnya kompetensi siswa/siswi dalam pemrograman C/C++ dan algoritma, diharapkan SMK Yadika 4 dapat melahirkan lulusan-lulusan yang siap dan mampu berkontribusi dalam industri teknologi informasi di masa depan.
Classification of Rlderly Health Using K-Nearest Neighbor Comparison, Naive Bayes and Decision Tree Kusuma, Alviant Chandra; Soetanto, Hari
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/2q4a1524

Abstract

Health and nutrition in the elderly play a crucial role in determining the quality of human resources, especially for the elderly themselves. The ageing process causes a decrease in the ability of body tissues to regenerate, making the elderly more vulnerable to infections and organ damage. Indonesia is currently experiencing an increase in the number of elderly, from 18 million people (7.56%) in 2010 to 25.9 million people (9.7%) in 2019, and is predicted to reach 48.2 million people (15.77%) in 2035. This study aims to determine the most effective algorithm for identifying the nutritional status of the elderly, by comparing three algorithms, namely Decision Tree, K-Nearest Neighbor (KNN), and Naïve Bayes. The methodology applied is CRISP-DM, and algorithm performance evaluation is carried out using the accuracy metric of the Confusion Matrix. The results showed that Decision Tree achieved the highest accuracy (95.55%), followed by Naïve Bayes (94.18%) and KNN (94.01%). The combination of algorithms provides optimal results because each algorithm can capture different patterns in the data, so the integration of the results can reduce errors and increase accuracy in the classification of the nutritional status of the elderly.
IMPLEMENTASI KRIPTOGRAFI UNTUK MELINDUNGI INFORMASI TRANSAKSI PADA E-COMMERCE MENGGUNAKAN METODE CAESAR CIPHER DAN RC4 Paulus Vidorosa Pakan; Hari Soetanto
JIFOSI Vol. 6 No. 2 (2025): Enhancing Information System Security and Governance in the Digital Transformat
Publisher : UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jifosi.v6i2.483

Abstract

Perkembangan teknologi informasi dan komunikasi telah menyebabkan peningkatan signifikan dalam jumlah transaksi yang dilakukan melalui website e-commerce, menawarkan kemudahan dalam berbelanja namun juga meningkatkan risiko keamanan data. Kejadian seperti kebocoran data besar-besaran di beberapa platform e-commerce terkemuka telah menyoroti kebutuhan mendesak untuk perlindungan data yang lebih efektif. Dalam upaya meningkatkan keamanan ini, penelitian ini mengimplementasikan kriptografi dengan menggunakan metode Caesar Cipher dan RC4, diuji melalui enkripsi dan dekripsi data bukti bayar pengguna dalam format JPEG, JPG, PNG, DOCX, XLSX, dan PDF. Proses enkripsi rata-rata membutuhkan waktu sekitar 0,025364685 detik, sementara dekripsi membutuhkan waktu 0,036693764 detik. Tingkat keberhasilan proses enkripsi dan dekprisi adalah 100% karena selama uji coba belum pernah gagal. Kedua metode tersebut terbukti efisien dalam menjaga kerahasiaan, integritas, dan otentikasi informasi transaksi yang dipertukarkan, memberikan solusi yang tidak hanya meningkatkan keamanan tetapi juga efisiensi dalam operasional e-commerce. Implementasi dari Caesar Cipher dan RC4 menawarkan perlindungan yang robust terhadap penyadapan, pencurian data, dan manipulasi oleh pihak yang tidak berwenang, mengarah pada peningkatan kepercayaan pengguna dan adopsi yang lebih luas dari layanan e-commerce.
Prototype of Internet of Things-Based Control System Using Telegram with Bot API Method Ferdin Permana Putra; Sahril Sabirin; Hari Soetanto
Jurnal Syntax Transformation Vol 6 No 2 (2025): Jurnal Syntax Transformation
Publisher : CV. Syntax Corporation Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/jst.v6i2.1055

Abstract

This research aims to develop and implement an Internet of Things (IoT)-based device control system using Telegram with the Bot API method. This system is designed so that users can control electronic devices, such as lights and fans, remotely and in real-time through the Telegram application. The method used in this research is experimental, testing the reliability and performance of the system under various network conditions. The NodeMCU ESP8266 is used as the main microcontroller that connects the device with the internet network, while the Relay Module is used to control electrical devices. The test results show that the system works well on a sTable Wi-Fi connection, with an average response time of 2-3 seconds and a 100% success rate. However, on weak Wi-Fi connections and cellular data, there was a drop in performance with longer response times and a slight decrease in success rate. The main advantage of this system is its ease of use, which only requires the Telegram messaging app to be accessed from anywhere. Nonetheless, the system has drawbacks, such as dependence on a sTable internet connection and a limited number of devices that can be controlled. This research successfully developed an efficient and practical IoT-based device control system. The system provides a solution for better management of electronic devices, with the potential to be further developed to improve device performance and compatibility.
Fuzzy Tahani Model for Selection Journal Computer Sciences and Technology (Sinta 2): Across Indonesia Wantoro, Agus; Hari Soetanto; Dikpride Despa; Tahta Herdian Andika
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i3.1787

Abstract

Publication of scientific articles in the national journal Science and Technology (Sinta) has become an obligation for academics such as students and lecturers. For students, publication is one of the requirements for graduation. For lecturers, scientific publication is a requirement for promotion or academic level (JA). Several levels of JA are Assistant Expert, Lecturer, Senior Lecturer and Professor. Lecturers who will apply for promotion to Senior Lecturer are required to publish in the Sinta 2 Journal. To be able to find a suitable journal, there are many considerations, if you choose the wrong journal, the article can be rejected. In choosing a journal, authors generally look at information such as publication costs, number of articles published, number of article publication frequencies, and review process time. Sometimes authors need ambiguous information such as the number of articles with many categories, low costs, high publication frequencies, and fast review times. The approach to this problem can use the Tahani fuzzy model database. This study applies the Tahani fuzzy model to model Sinta 2 journal data in the computer field to provide new findings and facilitate the selection of journals that match the criteria. This research needs to be done to provide useful information for several parties. For the author, this research is useful as a reference for selecting journals according to the criteria sought. For journal managers or editors, this information can increase the number of articles to be published, and for further researchers, this research will be information and reference material regarding journal selection research
Feature Selection and Class Imbalance Machine Learning for Early Detection of Thyroid Cancer Recurrence: A Performance-Based Analysis Wantoro, Agus; Caesarendra, Wahyu; Syarif, Admi; Soetanto, Hari
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.758

Abstract

Early detection of thyroid cancer recurrence is a crucial factor in patient survival and treatment effectiveness. Misdetection results in disease severity, high cost, recovery time, and decreased service quality. In addition, the main challenges in developing a Machine Learning (ML)-based detection decision support system are class imbalance in medical data and high feature dimensions that can affect model accuracy and efficiency. This study proposes a feature selection-based approach and class imbalance handling to improve the performance of early detection of Thyroid cancer. Several feature selection techniques, such as Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), and Chi-Square (CS), can select features based on weighted ranking. In addition, to overcome the imbalanced class distribution, we use the Synthetic Minority Over-Sampling Technique (SMOTE). ML classification models such as k-NN, Tree, SVM, Naive Bayes, AdaBoost, Neural Network (NN), and Logistic Regression (LR) are tested and evaluated based on a confusion matrix, including accuracy, precision, recall, time, and log loss. Experimental results show that the combination of imbalanced class handling strategies significantly improves the prediction performance of ML algorithms. In addition, we found that the combination of CS+NN feature selection techniques consistently showed optimal performance. This study emphasizes the importance of data pre-processing and proper algorithm selection in the development of a machine learning-based thyroid cancer detection system.
Evaluating The Impact of Social Media Sentiment on University Enrollment Decisions Using Machine Learning Classifier Painem, Painem; Soetanto, Hari; Solichin, Achmad; Nair, Anju A
JURNAL INFOTEL Vol 18 No 1 (2026): February
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v18i1.1390

Abstract

Public sentiment expressed through social media is increasingly recognized as a potential factor influencing higher education enrollment decisions. This study investigates whether sentiments on Twitter regarding Universitas Budi Luhur correlate with the number of new student admissions. To achieve this, tweet data were collected and analyzed using four supervised machine learning algorithms—Support Vector Classifier (SVC), Naïve Bayes, K-Nearest Neighbor (KNN), and Logistic Regression (LR)—combined with two lexicon-based sentiment dictionaries: SentiWord and InSet. Experimental results demonstrate that the SentiWord-based approach consistently outperformed the InSet-based approach across all models, with the SVC-SentiWord combination achieving the highest F1-score of 0.86. Despite the strong performance of these models in classifying sentiment, correlation analysis reveals no statistically significant relationship between Twitter sentiment and actual student enrollment trends. These findings underscore the effectiveness of lexicon-enhanced machine learning in sentiment analysis while raising important questions about the real-world impact of online sentiment on university admissions.
Co-Authors Achmad Solichin Admi Syarif Agus Wantoro Ahmad Fitrah Maulana Alfiansyah, Deden Moh Anidnya Putri Pradiptha Anindya Putri Pradiptha Ansis, Syukry Ardiane Rossi Kurniawan Maranto Asmarayani , Ilham Fikri Dwi Carla, Antoni Dade Reindra Dikpride Despa Dwi Agung Yanumatrajaya Dwi Kristanto Ferdin Permana Putra Joko Christian Chandra Kusuma, Alviant Chandra Laureen Laureen Listyaningsih, Endang Palupi Nair, Anju A Nicolas, Prayogi Perdana Painem, Painem Paulus Vidorosa Pakan Prabandalu Enggar Wiraswendro Pradipta, Nathanael Yudhistira Pradiptha, Anindya Putri Prasetiyo, Ari Budi Putra, Donny Anggara Retyanto Wardoyo Rossi, Ardiane Rusdah Rusdah Sahril Sabirin Samekto Wibowo Sri Hartati Suryadewiansyah, Muhammad Kamil Tahta Herdian Andika Talumepa, Renaldi Valentino Utomo Budiyanto Wahyu Caesarendra Wahyudi Wahyudi Wibowo, Danang Harito Yuga Prasetyo Yuliazmi, Yuliazmi