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All Journal Jurnal GEMATIKA Jurnal JSIKA Journal JCONES Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Journal of Applied Data Sciences Jurnal Sistem Informasi Universitas Dinamika International Journal Research on Metaverse
Jusak Jusak
STMIK Stikom Surabaya
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Other

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A novel women's ovulation prediction through salivary ferning using the box counting and deep learning Pratikno, Heri; Zamri Ibrahim, Mohd; Jusak, Jusak
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5847

Abstract

There are several methods to predict a woman's ovulation time, including using a calendar system, basal body temperature, ovulation prediction kit, and OvuScope. This is the first study to predict the time of ovulation in women by calculating the results of detecting the fractal shape of the full ferning (FF) line pattern in salivary using pixel counting, box counting, and deep learning for computer vision methods. The peak of a woman's ovulation every month in her menstrual cycle occurs when the number of ferning lines is the most numerous or dense, and this condition is called FF. In this study, the computational results based on the visualization of the fractal shape of the salivary ferning line pattern from the pixel-counting method have an accuracy of 80%, while the fractal dimensions achieved by the box-counting are 1.474. On the other hand, using the deep learning image classification, we obtain the highest accuracy of 100% with a precision value of 1.00, recall of 1.00, and F1-score 1.00 on the pre-trained network model ResNet-18. Furthermore, visualization of the ResNet-34 model results in the highest number of patches, i.e., 586 patches (equal to 36,352 pixels), by applying fern-like lines pattern detection with windows size 8x8 pixels.
Implementasi Pengamanan Transmisi Sinyal EKG (Elektrokardiogram) secara Daring dengan Metode Anonimasi JUSAK, JUSAK; SETIAWAN, BRAMASTA AGNANDA; SOLEHUDIN, SONY; PUSPASARI, IRA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 7, No 1: Published January 2019
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v7i1.85

Abstract

ABSTRAKData World Health Organization (WHO) pada tahun 2014 menunjukkan bahwa di Indonesia sebanyak 37% dari seluruh penyebab kematian adalah penyakit yang berhubungan dengan jantung. Kehadiran teknologi dan pemanfaatan Internet of Things (IoT) diharapkan dapat membantu mengurangi resiko kematian akibat penyakit jantung tersebut. Pada penelitian ini, pengukuran dan pengamatan sinyal jantung melalui tele-auskultasi sinyal elektrokardiogram (EKG) dilakukan. Untuk mengamankan sinyal EKG dalam proses transmisi melalui jaringan Internet digunakan metode anonimasi sinyal berbasis algoritma Jusak-Seedahmed. Hasil pengujian menunjukkkan bahwa algoritma Jusak-Seedahmed dapat melakukan proses anonimasi dan proses rekonstruksi sinyal dengan baik. Pengujian korelasi silang antara sinyal hasil rekonstruksi dan sinyal EKG asli sebelum anonimasi menghasilkan korelasi sebesar 1 pada lag=0. Sinyal EKG hasil rekonstruksi ditampilkan dalam aplikasi mobile untuk memudahkan analisis oleh dokter.Kata kunci: elektrokardiogram, keamanan, anonimasi, IoT, FFT ABSTRACTBased on the latest data released by the World Health Organization in 2014, deaths caused by cardiovascular disease in 2012 have reached 37% of the total number of non-communicable diseases deaths in Indonesia. Therefore, it is expected that the applications of the Internet of Things (IoT) might be used to reduce the risk of death due to the heart related problems. In this research, a tele-auscultation technique for measuring and monitoring electrocardiogram (ECG) signal was built. To secure transmission of the ECG signal over the Internet, we implemented a recently proposed Jusak-Seedahmed algorithm. Our examinations showed that the algorithm performed the anonymization and reconstruction processes well. Crosscorrelation analysis showed that correlation between the reconstructed and the original ECG signal at lag=0 was 1. Furthermore, a mobile-based application had been built to display the reconstructed ECG signal for further analysis.Keywords: electrocardiogram, security, anonimization, IoT, FFT
Fine-Grained Sentiment Analysis Approach on Customer Reviews Based on Aspect-Level Emotion Detection Paramita, Adi Suryaputra; Jusak, Jusak
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.964

Abstract

In the era of digital platforms, customer reviews constitute a vital resource for understanding user sentiment and perception toward products and services. Traditional sentiment analysis methods predominantly operate at the document or sentence level, often missing fine-grained emotional cues tied to specific product or service aspects. To address this limitation, this study proposes a novel Fine-Grained Sentiment Analysis (FGSA) framework that performs aspect-level sentiment classification using a joint learning approach. The proposed model employs a hybrid deep learning architecture that integrates transformer-based contextual encoders with Bidirectional Long Short-Term Memory (Bi-LSTM) layers. This design allows the model to capture both rich contextual semantics and sequential dependencies a combination that has not been widely adopted in existing FGSA research. Additionally, we introduce a new annotated dataset of 5,000 customer reviews spanning multiple domains (electronics, food and beverages, and general services), enabling robust training and evaluation. Experimental results show that the model outperforms standard baselines, achieving an F1-score of 82.0% for aspect extraction and an accuracy of 79.8% for sentiment classification. Further analysis reveals consistent patterns, such as positive sentiments linked to design and quality, and negative sentiments associated with customer service and delivery. These insights highlight the practical value of aspect-level sentiment modelling. The key contribution of this work is the integration of a transformer-Bi-LSTM joint architecture for aspect-based sentiment analysis, supported by a domain-diverse benchmark dataset. This framework enhances the interpretability and granularity of sentiment insights and sets a foundation for future research in multilingual and multimodal contexts.
Pengendali Suhu pada Proses Pasteurisasi Susu dengan Menggunakan Metode PID dan Metode Fuzzy Sugeno Triwidyastuti, Yosefine; Nizar, Muhammad; Harianto, Harianto; Jusak, Jusak
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 4: Agustus 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3350.834 KB) | DOI: 10.25126/jtiik.2019641068

Abstract

Proses pasteurisasi berfungsi untuk membunuh bakteri patogen yang dapat mengganggu kesehatan. Selain itu proses pasteurisasi juga bermanfaat untuk memperpanjang masa susu tidak rusak sehingga kualitas susu dapat dipertahankan sampai jangka waktu tertentu. Pada penelitian pengabdian masyarakat ini proses pasteurisasi susu dengan model low temperature long time (LTLT) dibangun dengan menggunakan pengendali PID dan pengendali Fuzzy. Model LTLT dipilih karena adanya kebutuhan masyarakat untuk dapat mencampur susu dengan berbagai perasa selama proses pasteurisasi berlangsung. Tujuan akhir dari penambahan perasa pada susu adalah untuk meningkatkan daya jual dari susu pasteurisasi. Berdasarkan hasil pengujian didapatkan bahwa sistem pengendali PID dengan nilai  K_P=31,8; K_I=117,8; K_D=4,3 memberikan respon lebih cepat daripada sistem pengendali Fuzzy berdasarkan pengukuran indikator waktu tunda, waktu naik, waktu puncak dan waktu penetapan. Sebaliknya sistem pengendali Fuzzy menghasilkan nilai MSE lebih kecil daripada sistem pengendali PID yang menunjukkan bahwa sistem pengendali Fuzzy lebih akurat daripada sistem pengendali PID dalam proses pasteurisasi susu. Hasil pengujian laboratorium terhadap susu hasil proses pasteurisasi menunjukkan bahwa jumlah cemaran mikroba telah turun pada jumlah sesuai dengan standar SNI pada saat yang sama kualitas susu hasil proses pasteurisasi tetap terjaga. AbstrackMilk pasteurization process has benefit for reducing pathogenic bacteria that may harm people’s health. At the same time, this process can be used to maintain the milk quality for long period of time. In this research, a milk pasteurization process that based on the low temperature long time (LTLT) was built utilizing the Proportional-Integral-Derivative and the Fuzzy system methods. The LTLT method was chosen in this project due to the need to blend the pasteurized milk with several type of food flavoring to increase the selling power of the pasteurized milk. Therefore, it needs longer pasteurization time. Based on the 30 trials of examination, it showed that the PID controller with values of  was able to provide a faster system response time compared to the Fuzzy controller. The measurement was done utilizing several indicators including delay time, rise time, peak time as well as settling time. In contrast, the Fuzzy controller produced a smaller mean squared error (MSE) compared to the PID controller showing that the Fuzzy controller produced smaller error fluctuation in the milk pasteurization process. Nevertheless, the results showed that both controllers exhibited MSE lower than , it indicates that both controllers could maintain milk temperature at the range of the standardized milk pasteurization process. Moreover, laboratory examination showed that using both pasteurization methods the number of coliform bacteria have been decreased to meet with the SNI standard and at the same time it was able to maintain the quality of the milk.
Predicting Player Performance in Valorant E-Sports using Random Forest Algorithm: A Data Mining Approach for Analyzing Match and Agent Data in Virtual Environments Paramita, Adi Suryaputra; Jusak, Jusak
International Journal Research on Metaverse Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i4.39

Abstract

This study presents a data-driven approach to predict player performance in Valorant, an increasingly popular e-sport, using a Random Forest machine learning model. As e-sports continue to evolve within the metaverse, the need for strategic optimization and player selection has become critical. By analyzing a dataset containing player statistics from the Valorant Champion Tour (VCT), we aimed to predict player Rating, a key performance indicator. The dataset includes various metrics such as Kills Per Round, Average Combat Score (ACS), Clutch Success Ratio, and Kills:Deaths. After preprocessing the data, which involved handling missing values and feature engineering, the dataset was split into training and testing sets (80% and 20%, respectively). The Random Forest model, with 100 estimators and a maximum depth of 10, was trained on the processed data. The model's performance was evaluated using regression metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). The results demonstrated that the model could predict player performance with a high degree of accuracy, with an R² value of 0.8831, meaning it explained 88.31% of the variance in player ratings. Furthermore, Kills Per Round emerged as the most significant feature, followed by Kill, Assist, Trade, Survive Ratio and Average Damage Per Round. These insights suggest that key metrics like kills and damage output are crucial for predicting player success. This study not only provides a comprehensive framework for predicting Valorant player performance but also demonstrates the potential of data mining in optimizing e-sports strategies. The findings contribute to the growing body of research on virtual gaming environments and offer actionable insights for teams in the metaverse, enabling data-driven decision-making to enhance performance and strategic outcomes.
Co-Authors A. B. Tjandrarini Adani, Muhammad Faizin Aderusman, Rizky Adi Suryaputra Paramita Agus Ariyanto Agus Dwi Churniawan, Agus Dwi Alfian Angga Pradika Alim Rahmat Rido Anggara, Faris Widi Anjik Sukmaaji Atmaja, Bhagus Sugamayana Dwi Atmaja, Bhagus Sugamayana Dwi Budi Hari Nugroho Burhanuddin Surya Putra David Palguna Djojosuroto, Muh.Rahmat Effendy, Edwin David Eka Sari Oktarina Ekinasti, Anggi Tiara Citra Erwin Sutomo Feri Setiawan Adinata Firdaus, Deddy Mohammad Fumio Highy Suparlan Hakim, Arnaz Malikul Hakim, Arnaz Malikul Harianto Harianto Harianto, Gaguk Hasyim, Dealis Dinamika Henry Bambang Setyawan Heri Pratikno Hikam, Dicky Sirojul Hoky Ajicahyadi I Dewa Gede Rai Mardiana Ignatius Adrian Mastan Imam Fauzi Ira Puspasari Irene Rizky Andini Jap Fen Dy, Jap Fen Julianto Lemantara Kurnia, Lassa Nusti Kusbiono Wisnu Pambudi Kusuma, Otniel Reza Kusuma, Yusuf Budi M. J. Dewiyani Sunarto Mardianto Basuki Martinus Sony Erstiawan Mochammad Arifin Muhammad Nizar Muhammad Syakir Kautsar Mustafa, Putra Yoga Dwiangga Mustafa, Putra Yoga Dwiangga Nahusuly, Nova Nahusuly, Nova Nanda Surya Setiawan Octavianus Wijaya Oktaviyani, Tri oktorianto, kevin widoni Pamungkas, Johan Pantjawati Sudarmaningtyas Pauladie Susanto Phompi Andinata Pietter, P. Earl Pietter, P. Earl Pradana, Yulyus Effendi Prastyo, Pangky Ari Wibowo Putra, Febri Pradana Putra, Febri Pradana Putro, Abid Eka Sukatno Ramadhanis, Puspayati Ramadhanis, Puspayati Ramzi, Muhammad Ramzi, Muhammad Rendi Haris Nofianto Reppy Reisa Rido, Alim Rahmat Rido, Alim Rahmat Rizky Ananto Putri Rizky Kurniawan Robiyanto Robiyanto, Robiyanto Rohmat Solikin Rudi Santoso, Rudi Saputro, Dedy Tri Saraswati, Dyah Lestari Sembiring, Rinawati Septian Adi Herlambang, Septian Adi setiawan, ashari SETIAWAN, BRAMASTA AGNANDA SETIAWAN, BRAMASTA AGNANDA SOLEHUDIN, SONY SOLEHUDIN, SONY Suarjaya, I Gede Sugiarto, Wahyu Rokhman Susanti, Ririn Susanti, Ririn Susanto, Ubaidillah Susianto Tri Resmana Syafi'i, Imam Tajuddin Akbar Tegar Heru Susilo Teguh Sutanto Triwibowo, Aris Viranda, Rizkyana Surya Vivine Nurcahyawati Wahyu Andy Yulianto Wahyu Setiawan Weny Indah Kusumawati Wirandha Ryan Pratama Wiyono, Reynaldi Arfian Agus Yoga Eka Saputra Yosefine Triwidyastuti Zamri Ibrahim, Mohd