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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknoin JURNAL SISTEM INFORMASI BISNIS Jurnal Buana Informatika Bulletin of Electrical Engineering and Informatics Journal of Education and Learning (EduLearn) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Algoritma Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika Journal of Information Systems Engineering and Business Intelligence Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi Journal of Applied Geospatial Information JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling Indonesian Journal of Computing and Modeling JURIKOM (Jurnal Riset Komputer) Jurnal Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Abdi Insani Abdimasku : Jurnal Pengabdian Masyarakat Aiti: Jurnal Teknologi Informasi Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JOINTER : Journal of Informatics Engineering IJECS: Indonesian Journal of Empowerment and Community Services International Journal of Engineering, Science and Information Technology International Journal of Community Service Jurnal Impresi Indonesia Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Algoritma Malcom: Indonesian Journal of Machine Learning and Computer Science Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat Jurnal Rekayasa elektrika Jurnal INFOTEL Scientific Journal of Informatics INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) JOT
Kristoko Dwi Hartomo
Universitas Kristen Satya Wacana
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Sentiment Analysis of the Free Nutritious Meal Program Using IndoBERT and RCNN Methods Ancilla Nebrisca Valonika; Kristoko Dwi Hartomo
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/qg9bfb89

Abstract

This study examines public sentiment regarding the Free Nutritious Meal Program through a deep learning-based sentiment classification methodology applied to X and TikTok. The suggested method uses a hybrid IndoBERT RCNN architecture, with IndoBERT being used to extract features and RCNN being used to classify sentiment. There are 10,000 comments from each platform in the dataset. These comments went through preprocessing and sentiment labeling steps. Model evaluation was conducted using stratified K-fold cross-validation with different combinations of learning rate, batch size, and epochs. The best configuration achieved an accuracy and F1-score of 78% on X and 83% on TikTok. The model performs well in identifying overall sentiment patterns, although neutral sentiment remains challenging to classify, particularly in X data containing sarcastic or indirect language. These findings provide empirical insights into cross-platform sentiment characteristics and highlight the potential of this approach for testing sentiment monitoring strategies across platforms.
Comparison of IDW and Kriging Interpolation Methods Using Geoelectric Data to Determine the Depth of the Aquifer in Semarang, Indonesia Brilliananta Radix Dewana; Sri Yulianto Joko Prasetyo; Kristoko Dwi Hartomo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 2 (2022): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i2.23260

Abstract

Several areas in Semarang City have been unable to get a clean water supply through the Local Water Company (PDAM) channel. One of the solutions that can be done to overcome this problem is by utilizing groundwater, which can be obtained by building a deep well made to obtain rock layers that can accommodate and drain groundwater (aquifer layer). To find out the approximate depth of the aquifer layer, it is necessary to conduct a preliminary investigation before drilling. There are so many methods that can be done, and one of them is by using the geoelectric method. After using the geoelectric method, we can determine the distribution of the depth of the aquifer in Semarang City by using interpolation analysis. In this study, the IDW and Kriging interpolation methods were used. The two methods were then compared to show the difference in the distribution of aquifer depths in areas that lack clean water using the two interpolation methods above. Besides that, we are using RMSE and MAPE analysis to find the error rate of the two methods. The results obtained were the RMSE of the IDW and Kriging methods amounting to 5,829 and 5,433, and the MAPE results were 10.90% and 10.34%. Based on this, the Kriging method tends to have better results when interpolating using geoelectric data. With this research, it is hoped to provide knowledge to determine the most suitable interpolation method used in determining the depth of the aquifer and also can be used as an illustration of the depth of the aquifer in the area that lacked clean water in Semarang City, so that it can be used as a reference in estimating the design of deep good development more accurately.
Evaluating Sampling Techniques for Healthcare Insurance Fraud Detection in Imbalanced Dataset Joanito Agili Lopo; Kristoko Dwi Hartomo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.25929

Abstract

Detecting fraud in the healthcare insurance dataset is challenging due to severe class imbalance, where fraud cases are rare compared to non-fraud cases. Various techniques have been applied to address this problem, such as oversampling and undersampling methods. However, there is a lack of comparison and evaluation of these sampling methods. Therefore, the research contribution of this study is to conduct a comprehensive evaluation of the different sampling methods in different class distributions, utilizing multiple evaluation metrics, including , , , Precision, and Recall. In addition, a model evaluation approach be proposed to address the issue of inconsistent scores in different metrics. This study employs a real-world dataset with the XGBoost algorithm utilized alongside widely used data sampling techniques such as Random Oversampling and Undersampling, SMOTE, and Instance Hardness Threshold. Results indicate that Random Oversampling and Undersampling perform well in the 50% distribution, while SMOTE and Instance Hardness Threshold methods are more effective in the 70% distribution. Instance Hardness Threshold performs best in the 90% distribution. The 70% distribution is more robust with the SMOTE and Instance Hardness Threshold, particularly in the consistent score in different metrics, although they have longer computation times. These models consistently performed well across all evaluation metrics, indicating their ability to generalize to new unseen data in both the minority and majority classes. The study also identifies key features such as costs, diagnosis codes, type of healthcare service, gender, and severity level of diseases, which are important for accurate healthcare insurance fraud detection. These findings could be valuable for healthcare providers to make informed decisions with lower risks. A well-performing fraud detection model ensures the accurate classification of fraud and non-fraud cases. The findings also can be used by healthcare insurance providers to develop more effective fraud detection and prevention strategies.
Perancangan UI/UX Logbook Keperawatan Rumah Sakit Menggunakan Metode Design Thinking: UI/UX Design for Hospital Nursing Logbook Using Design Thinking Method Akbar, Rifqi Nabil; Hartomo, Kristoko Dwi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2687

Abstract

Rumah Sakit Dr. Oen Solo Baru merupakan institusi kesehatan di bawah Yayasan Kesehatan Panti Kosala yang berkomitmen terhadap peningkatan mutu pelayanan keperawatan. Untuk mendukung hal tersebut, diperlukan sistem dokumentasi logbook keperawatan yang mampu meningkatkan efisiensi, akurasi data, dan transparansi dalam evaluasi kinerja perawat. Namun, proses pencatatan logbook masih menghadapi kendala, seperti pencatatan yang kurang terstruktur, keterlambatan pengisian, kesulitan merekapitulasi data, serta pemantauan oleh kepala unit yang belum optimal. Oleh karena itu, penelitian ini berfokus pada perancangan sistem informasi logbook keperawatan berbasis web sebagai solusi atas permasalahan tersebut. Agar sistem yang dirancang tepat sasaran, penelitian ini mengadopsi pendekatan Design Thinking. Melalui metode ini, perancangan User Interface (UI) dan User Experience (UX) diarahkan untuk mengatasi hambatan operasional serta memenuhi kebutuhan tenaga medis di lapangan. Untuk memastikan kualitas desain, sistem dievaluasi menggunakan System Usability Scale (SUS). Hasil pengujian menunjukkan skor SUS sebesar 84,03 dari perawat pelaksana dan 85 dari kepala unit. Pencapaian ini menempatkan rancangan sistem pada grade A dengan adjective rating excellent dan acceptability range acceptable, yang menunjukkan bahwa sistem memiliki tingkat kebergunaan yang tinggi, memuaskan, dan mudah diterima oleh pengguna
Management of Traditional Business into Modern: from Microsoft Excel to Deep Learning for prototyping classification Swiftlet’s nests Hanna Arini Parhusip; Suryasatriya Trihandaru; Kristoko Dwi Hartomo; Karina Bianca Lewerissa; Linda Ariany Mahastanti; Djoko Hartanto
International Journal Of Community Service Vol. 4 No. 2 (2024): May 2024 (Indonesia - Ethiopia )
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijcs.v4i2.268

Abstract

In this article, the transformation of traditional management of Swiftlet’s nests into modern business is proposed. Traditional business means that data management of Swiftlet’s nests is done manually, sorted by recording in Microsoft Excel. This is done by PT Waleta Asia Jaya, a company engaged in processing Swiftlet’s nests. This sorting is done because the number of feathers in the Swiftlet’s nests determines the price and cost of workers in processing feather cleaning. In addition, the shape of the Swiftlet’s nests needs attention. However, because it is complex, sorting is done simpler. Originally, Swiftlet’s nests were sorted into 50 categories. To facilitate sorting, deep learning is used with the SSD Mobile Net V2 algorithm as an algorithm to classify into 7 categories based on feather intensity. The device is still a prototype that shows an 85% accuracy rate but has been quite helpful in the process of purchasing Swiftlet’s nests before processing.
Analisis Metode Klasifikasi Nasabah Potensial dalam Membuka Deposito Jangka Panjang Melalui Telemarketing Menggunakan Metode Gradient Boosting Classifier Michael Richard Takakobi; Kristoko Dwi Hartomo
Jurnal Impresi Indonesia Vol. 4 No. 5 (2025): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v4i5.6688

Abstract

Penelitian ini bertujuan untuk melihat klasifikasi nasabah dalam membuka sebuah deposito jangka panjang. Penelitian ini akan menggunakan algoritma Machine Learning, yaitu Gradient Boosting Classifier. Dataset yang digunakan diambil dari arsip dataset UC Irvine Machine Learning Repository yang mencangkup 41.188 sampel dengan 20 variabel. Dataset Kampanye bank di Portugal menggunakan metode penawaran dilakukan secara jarak jauh atau tidak langsung yang biasa disebut dengan Telemarketing. Nasabah dalam dataset terdiri dari berbagai latar belakang. Penelitian memberikan hasil baik dalam klasifikasi menggunakan metode Gradient Boosting Classifier, metode ini dikombinasikan dengan teknik random oversamping untuk mengatasi data imbalance. Penelitian menghasilkan nilai ROC-AUC sebesar 0.81. Hasil penelitian juga memberikan informasi yang dapat digunakan dalam pengambilkan Keputusan terkait dengan kampanye telemarketing selanjutnya. Penelitian ini merekomendasikan penerapan model ini untuk meningkatkan efisiensi telemarketing dengan menargetkan nasabah berpotensi, sekaligus mengurangi biaya operasional.
Manajemen Risiko Trading Aset Kripto Melalui Pendekatan Bet Sizing yang Diadaptasi dari Game Theory Jason Evan Hendarko; Kristoko Dwi Hartomo
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3440

Abstract

The high volatility and fat-tailed return distribution of cryptocurrency markets require a more adaptive risk management approach than conventional static models. This study aims to implement and evaluate the effectiveness of a bet-sizing model adapted from Game Theory using the Kelly Criterion as an adaptive risk management framework. A quantitative approach was applied to four major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and XRP—using 2020 to 2022 as the in-sample period and 2023 to 2025 as the out-of-sample evaluation period. The out-of-sample results demonstrate that the Kelly Criterion-based bet-sizing model outperformed the traditional risk management approach, generating a total return of one hundred thirty-eight point sixty percent and a Sharpe Ratio of 1.34, compared with twelve point eleven percent and 1.22, respectively, for the conventional model. However, this superior performance was accompanied by a substantially larger Maximum Drawdown (MDD) of thirty-three point thirty-three percent, compared with four point twenty-three percent under the traditional approach. These findings indicate that an adaptive transaction-level risk allocation strategy is better able to respond to the dynamic characteristics of cryptocurrency markets. Nevertheless, the relatively limited number of trades and the restricted evaluation period make the bet-sizing model sensitive to changes in sample characteristics, particularly under more extreme market conditions. Despite these limitations, the study incorporates in-sample and out-of-sample validation, as well as transaction costs and slippage, into the performance evaluation, providing a more realistic assessment of the proposed adaptive risk management strategy.
Pengelolaan Perlindungan Data Pribadi Menggunakkan MongoDB Change Streams Untuk Sistem Notifikasi Real-Time Timothy Arif Kurniawan; Kristoko Dwi Hartomo
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6134

Abstract

Perkembangan teknologi memberikan dampak positif maupun dampak negatif bagi masyarakat. Salah satu bentuk dampak negatif perkembangan teknologi adalah munculnya aktivitas pencurian data. Hal tersebut merupakan aktivitas yang dapat menghambat kegiatan masyarakat khususnya kalangan organisasi atau perusahaan. Penelitian ini bertujuan untuk membuat sebuah aplikasi pengelolaan data yang dapat menampilkan riwayat aktivitas operasi data yang terjadi pada collection serta menampilkan response dari proses pengelolaan data dalam bentuk message notifikasi realtime. Untuk mendukung proses pengembangan aplikasi yang diinginkan, maka dibutuhkan teknologi yang dapat dimanfaatkan sebagai pendukung pengembangan aplikasi dan teknologi utama yang digunakkan adalah MongoDB Change Streams dan Websockets. Lalu untuk memastikan aplikasi telah bekerja sesuai dengan requirements user dan juga planning, maka aplikasi akan melalui tahap pengujian black box. Penelitian ini menghasilkan sebuah aplikasi pengelolaan data dengan notifikasi realtime serta riwayat log berbasis web. Dengan aplikasi tersebut, user dapat mengelola data serta melakukan pemantauan terhadap seluruh aktivitas pengelolaan data yang terjadi pada collection sehingga hal tersebut dapat mencegah terjadinya aktivitas pencurian data.
Machine Learning-Based Heart Failure Worsening Prediction Model to Build Self-Monitoring Prototype as an Effort to Prevent Readmissions and Maintain Quality of Life Untung Rahardja; Kristoko Dwi Hartomo; Indrajani Sutedja; Ardi Kho; Muhammad Farhan Kamil
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1467

Abstract

Heart failure is a long-term condition of great concern which calls for health care services in cycles. This significantly hampers quality of life for patients and increases costs for the healthcare systems. If the worsening of heart failure could be detected early, the intervention to prevent readmission could be employed, such that readmission would be avoided, enhancing the quality of life for the patient. Accordingly, the paper explains how such a model to predict the worsening of heart failure in patients who are at high risk of this condition has been developed. The model uses information gathered from the Electronic Health Records (EHRs) (Clinical Variables, Vitals, Test Results, and Demographics) to make accurate predictions on patients. As an effective and efficient approach towards achieving this goal, comparison of different algorithms such as random forests, support vector machines and gradient boosting has been employed towards the building of the final model. At this stage, the model is embedded into a user-friendly self-monitoring device, allowing the chronic heart failure patients to assess health indices on the fly with the help of the mobile app and wearable devices. This secondary prevention strategy makes patients more responsible for their health and decreases the number of patients readmitted to the hospital by increasing their functioning and well-being. The paper further projects the future development of other forms of treatment for chronic heart failure, especially at the first line, focusing primarily on the timing and succession.
Empirical Studies on the Relationship Between Wearable Stress Detection and Workplace Productivity Kristoko Dwi Hartomo; Muhammad Zaki; Gilang Kartika Hanum; Nur Silawati; Adele Valerry
Journal of Orange Technology Vol. 1 No. 1 (2024): October
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/jot.v1i1.1

Abstract

Workplace stress has been widely recognized as a critical factor influencing employee health, performance, and organizational outcomes. Recent advancements in wearable technologies provide real-time physiological data that open new opportunities for monitoring and managing stress in professional settings. This study aims to empirically investigate the relationship between wearable-based stress detection and workplace productivity, focusing on how continuous monitoring can enhance well-being and performance. A quantitative approach was employed with 250 participants across three corporate sectors, where wearable devices measured physiological indicators such as heart rate variability and skin conductance, while productivity was assessed through task completion rates and self-reported efficiency. Statistical analyses, including correlation, regression, and moderation analysis, were conducted to examine the strength of associations. Findings reveal a significant negative correlation between elevated stress levels and productivity metrics, while participants using wearable feedback interventions demonstrated improved stress awareness and a 15% increase in task efficiency compared to the control group. In conclusion, wearable stress detection presents a promising tool for enhancing workplace productivity by enabling proactive stress management, highlighting the importance of integrating technology, psychology, and organizational practices to foster healthier and more effective work environments.
Co-Authors Ade Iriani Adele Valerry Agata, Kristien Yuni Agus Bambang Nugraha Ahmad Ashifuddin Aqham Akbar, Rifqi Nabil Alexandra, Andrea Cellista Ancilla Nebrisca Valonika Ancilla Nebrisca Valonika Andeka Rocky Tanaamah Andriana, Myra Angelia Destriana Anggara Cahya Putra Anthony Y.M. Tumimomor April Firman Daru Ardi Kho Ariel Kristianto Arthur, Christian Aruperes, Viveca Grivenda Aryanata Andipradana Baali, Gabriel Megfaden Kenisa Bagaskara, Adyatma Andhika Bambang Ismanto Brilliananta Radix Dewana Chandra Husada Danny Manongga Danny Sebastian Dearmelliani Tarigan Desyandri Desyandri Dewi, Stefani Fransisca Dian Widiyanto Chandra Diky Candra Muria Pratama Djoko Hartanto Dwi Anggono Winarso Suparjo Putra Dwi Hosanna Bangkalang Eko Sediyono Enik Muryanti Estie Grace Melisa Sinulingga Evangs Evi Maria Ezra Julang Prasetyo Faudisyah, Alfendio Alif Gerry Santos Lasatira Gladiola Lavinia Ambayu Gogo Krisatyo Hanna Arini Parhusip Hanna Prillysca Chernovita Hanum, Gilang Kartika Hendry Hindriyanto Dwi Purnomo Indrajani Sutedja Indrajaya, Denny Irwan Sembiring Jason Evan Hendarko Joanito Agili Lopo Joanito Agili Lopo Johan Jimmy Carter Tambotoh Joseph Teguh Santoso Joshua Rondonuwu Juneth Manuputty Karina Bianca Lewerissa Karina Bianca Lewerissa Kevin Benedictus Simarmata Kevin Stevian Hermawan Kezia Sharent Kodoati Kuncoro, Wreda Agung Limbong, Josua Josen Alexander Linda Ariany Mahastanti Lobo, Murry Albert Agustin Magdalena Ariance Ineke Pakereng Martin Setyawan Martin Teddy Sihite Matheus Supriyanto Rumetna Michael Richard Takakobi Mila Chrismawati Paseleng Mozad Timothy Waluyan Muflihanto, Ezar Juan Muhammad Farhan Kamil Muhammad Rizky Ramadhan Muhammad Sholikhan Muhammad Zaki Nalbraint Wattimena Nicolas Evander Suhandi Nikhlis, Neilin Nina Setiyawati Nining Fitriani Nur Silawati nuranto, bogo Nurrokhman, Nurrokhman Nuzhah Al Waaidhoh Penidas Fodinggo Tanaem Prakoso, Hendri Suryo Pramudhita Tunjung Seta Prasetyo, Sri Yulianto Prasianto, Kornelius Reinand Purnomo, Andreas Wisnu Adi Purwanto Purwanto Raditya Ditto Aryaputra Radius Tanone Radjawane, Samy Rahmawati, Lutfi Raymond Elias Mauboy Rizaldi, Alexander Roy Armus Allu Sandy Pratama Saputro, Andreas Arga Rinjani Septian Silvianugroho Sinulingga, Yedija Sada Ukurta Sri Yulianto Sri Yulianto Joko Prasetyo Stevan Hamonangan Hardi Suhandi, Nicolas Evander Suharjo, Rahmat Abadi Sulistiawati, Anita Suryasatriya Trihandaru Sutarto Wijono T. Arie Setiawan P Teguh Wahyono Theopillus J. H. Wellem Timothy Arif Kurniawan Tri Harjani Tri Wahyuningsih Tridinatha, Zenitha Eunike Triloka Mahesti Tumbade, Marcho Oknivan Untung Rahardja Wahab, Nur Haliza Abdul Waliyuddin Rabbani, Imam Wibowo, Mars Caroline William, Kevin Hendra Winarko, Edi Wiwien Hadikurniawati Yessica Nataliani Yohan Maurits Indey