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All Journal Jurnal Gamma Jurnal Sain Veteriner MEDIA ELEKTRIKA Paramita: Historical Studies Journal Journal of Consumer Science Catharsis JOIV : International Journal on Informatics Visualization Journal of Consumer Sciences Jurnal SOLMA JTAM (Jurnal Teori dan Aplikasi Matematika) Khadimul Ummah Journal of Social Dedication MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Innovation in Business and Economics EDUMATIC: Jurnal Pendidikan Informatika Jurnal Perilaku dan Strategi Bisnis Abdi Masyarakat ASSET: Jurnal Manajemen dan Bisnis Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Prosiding Konferensi Nasional PKM-CSR Jurnal Begawe Teknologi Informasi SainsTech Innovation Journal Jurnal Karya Pengabdian Teknik Mesin Innovative: Journal Of Social Science Research Jurnal Kabar Masyarakat JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah Jurnal Pengabdian Kepada Masyarakat (JPKM) Jurnal Pengabdian Masyarakat dan Riset Pendidikan Infact: Jurnal Sains dan Komputer Emitor: Jurnal Teknik Elektro Jurnal Wicara Desa Edu Komputika Journal SainsTech Innovation Journal
Heri Wijayanto
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Tree-based Filtering in Pulse-Line Intersection Method Outputs for An Outlier-tolerant Data Processing Damarjati, Cahya; Trinanda Putra, Karisma; Wijayanto, Heri; Chen, Hsing-Chung; Nugraha, Toha Ardi
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.861

Abstract

Pulse palpation is one of the non-invasive patient observations that identify patient conditions based on the shape of the human pulse. The observations have been practiced by Traditional Chinese Medicine (TCM) practitioners since thousands of years ago. The practitioners measure the patient’s arterial pulses in three points of both patient wrists called chun, guan, and chy, then diagnose based on their knowledge and experience. Pulse-Line Intersection (PLI) method extract features of each pulse from the observed pulse wave sequence. PLI is performed by summing the number of intersections between the artificial line and the pulse wave. The method is proven in differentiating between hesitant with moderate pulse waves. As the method implemented in Clinical Decision Support System (CDSS) related to pulse palpation, some outlier data might emerge and affect the measurement result. Thus, outlier filtering is needed to prevent unnecessary prediction processes by machine learning (ML) models inside CDSS. This study proposed an outlier filtering model using a decision tree algorithm. This concept is designed by analyzing pulse features values and the chance of odd values combination. Then inappropriate values are excepted using several rules. Every pulse feature list that did not pass the filtering rule is categorized as outliers and were not included for further process. The proposed model works more efficiently than ML models dealing with outliers since this procedure is unsupervised learning with a small number of parameters. Overall, the proposed filtering method can be used in pulse measurement applications by eliminating outlier data that might decrease the performance of ML models.
Analyzing Coverage Probability of Reconfigurable Intelligence Surface-aided NOMA Widodo, Agung Mulyo; Wijayanto, Heri; Wijaya, I Gede Pasek Suta; Wisnujati, Andika; Musnansyah, Ahmad
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.2054

Abstract

Along with the explosive growth of wireless communication network users who require large frequency bands and low latency, it is a challenge to create a new wireless communication network beyond 5G. This is because installing a massive 5G network requires a large investment by network providers. For this reason, the authors propose an alternative beyond 5G that has better quality than 5G and a relatively lower investment value than 5G networks. This study aims to analyze the downlink of the cooperative non-orthogonal multiple access (NOMA) network, which is usually used in 5G, combined with the use of a reconfigurable intelligence surface (RIS) antenna with decode and forward relay mechanisms. RIS is processed with a limited number of objects utilizing Rayleigh fading channels. The scenario is created by a user who relays without a direct link for users near the base station and with a direct link for users far from the base station. Under the Nakagami-m fading channel, the authors carefully evaluated the probability of loss for various users as a function of perfect channel statistical information (p-CSI) utilizing simply a single input-output (SISO) system with a finite number of RIS elements. As a key success metric, the efficiency of the proposed RIS-assisted NOMA transmission mechanism is evaluated through numerical data on the outage probability for each user. The modeling outcomes demonstrate that the RIS-aided NOMA network outperforms the traditional NOMA network
A Conversion of Signal to Image Method for Two-Dimension Convolutional Neural Networks Implementation in Power Quality Disturbances Identification Berutu, Sunneng Sandino; Chen, Yeong-Chin; Wijayanto, Heri; Budiati, Haeni
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1529

Abstract

The power quality is identified and monitored to prevent the worst effects arise on the electrical devices. These effects can be device failure, performance degradation, and replacement of some device parts. The deep convolutional neural networks (DCNNs) method can extract the complexity of image features. This method is adopted for the power quality disruption identification of the model. However, the power quality signal data is a time series. Therefore, this paper proposes an approach for the conversion of a power quality disturbance signal to an image. This research is conducted in several stages for constructing the approach proposed. Firstly, the size of a matrix is determined based on the sampling frequency values and cycle number of the signal. Secondly, a zero-cross algorithm is adopted to specify the number of signal sample points inserted into rows of the matrix. The matrix is then converted into a grayscale image. Furthermore, the resulting images are fed to the two-dimension (2D) CNNs model for the PQDs feature learning process. When the classification model is fit, then the model is tested for power quality data prediction. Finally, the model performance is evaluated by employing the confusion matrix method. The model testing result exhibits that the parameter values such as accuracy, recall, precision, and f1-score achieve at 99.81%, 98.95%, 98.84, and 98.87 %, respectively. In addition, the proposed method's performance is superior to the previous methods. 
Text Classification Using Genetic Programming with Implementation of Map Reduce and Scraping Wedashwara, Wirarama; Irmawati, Budi; Wijayanto, Heri; Arimbawa, I Wayan Agus; Widartha, Vandha Pradwiyasma
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1813

Abstract

Classification of text documents on online media is a big data problem and requires automation. Text classification accuracy can decrease if there are many ambiguous terms between classes. Hadoop Map Reduce is a parallel processing framework for big data that has been widely used for text processing on big data. The study presented text classification using genetic programming by pre-processing text using Hadoop map-reduce and collecting data using web scraping. Genetic programming is used to perform association rule mining (ARM) before text classification to analyze big data patterns. The data used are articles from science-direct with the three keywords. This study aims to perform text classification with ARM-based data pattern analysis and data collection system through web-scraping, pre-processing using map-reduce, and text classification using genetic programming. Through web scraping, data has been collected by reducing duplicates as much as 17718. Map-reduce has tokenized and stopped-word removal with 36639 terms with 5189 unique terms and 31450 common terms. Evaluation of ARM with different amounts of multi-tree data can produce more and longer rules and better support. The multi-tree also produces more specific rules and better ARM performance than a single tree. Text classification evaluation shows that a single tree produces better accuracy (0.7042) than a decision tree (0.6892), and the lowest is a multi-tree(0.6754). The evaluation also shows that the ARM results are not in line with the classification results, where a multi-tree shows the best result (0.3904) from the decision tree (0.3588), and the lowest is a single tree (0.356).
Perancangan Ilustrasi Burung Merak sebagai Maskot Unik Alfin Digital Printing dalam Meningkatkan Brand Awareness Wijayanto, Heri; Maulana, Mochamad Rafli; Nanto, Abdian Desri; Budimansyah, Alif Laksana; Afif, Muhammad Dzihnin; Fadillah, Syifa
Innovative: Journal Of Social Science Research Vol. 5 No. 4 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i4.20523

Abstract

Alfin Digital Printing merupakan salah satu perusahaan jasa percetakan di Tangerang yang membutuhkan identitas visual kuat agar mampu bersaing di tengah industri percetakan yang kompetitif. Hingga kini, perusahaan belum memiliki maskot sebagai elemen visual khas yang dapat mewakili karakter, nilai, dan citra brand secara konsisten. Penelitian ini bertujuan untuk merancang ilustrasi maskot burung merak sebagai representasi visual Alfin Digital Printing, guna memperkuat brand awareness di kalangan pelanggan dan masyarakat luas. Metode yang digunakan dalam penelitian ini adalah pendekatan Design Thinking yang terdiri dari lima tahapan, yaitu Empathize, Define, Ideate, Prototype, dan Test. Data diperoleh melalui observasi, wawancara, serta analisis SWOT untuk memahami kebutuhan visual perusahaan. Hasil perancangan menunjukkan bahwa ilustrasi maskot burung merak dengan karakter ramah, warna cerah, dan atribut percetakan mampu memperkuat citra brand serta meningkatkan daya tarik promosi perusahaan. Maskot tersebut dirancang fleksibel agar dapat diterapkan di berbagai media promosi cetak dan digital. Kesimpulannya, maskot tidak hanya berfungsi sebagai elemen dekoratif, melainkan juga sebagai media komunikasi visual strategis yang efektif dalam membangun brand awareness dan citra positif Alfin Digital Printing. Kata Kunci: Maskot Ilustrasi, Burung Merak, Design Thinking, Brand Awareness, Identitas Visual.
Model Project-Based Visual Campaign Learning untuk Meningkatkan Literasi Media Promosi Digital Siswa SMK Grafika: Pengabdian Aiyah; Heri Wijayanto; Dewi Intan Kurnia; Zulfah Hasanah
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4881

Abstract

Perkembangan media digital telah mengubah paradigma strategi promosi, menuntut sumber daya manusia yang memiliki literasi visual, kemampuan berpikir strategis, serta keterampilan produksi konten kreatif yang adaptif. Sekolah Menengah Kejuruan (SMK) bidang grafika dan desain komunikasi visual memiliki peran strategis dalam menyiapkan lulusan yang siap menghadapi kebutuhan industri kreatif dan media digital. Namun, proses pembelajaran yang masih berorientasi pada teknis perangkat lunak sering kali belum terintegrasi dengan pemahaman strategi kampanye promosi secara komprehensif. Program Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk meningkatkan literasi media promosi digital siswa SMK Grafika melalui penerapan Model Project-Based Visual Campaign Learning. Model ini mengintegrasikan pembelajaran berbasis proyek dengan perancangan kampanye visual yang menekankan ilustrasi, identitas visual, dan pesan komunikasi yang strategis. Metode pelaksanaan meliputi tahap analisis kebutuhan, perancangan modul, pelaksanaan workshop berbasis proyek, pendampingan, serta evaluasi hasil karya siswa. Hasil kegiatan menunjukkan peningkatan pemahaman siswa terhadap konsep kampanye promosi digital, kemampuan merancang ilustrasi yang komunikatif, serta konsistensi identitas visual dalam media sosial. Model ini dinilai efektif sebagai pendekatan pembelajaran aplikatif yang relevan dengan kebutuhan industri kreatif dan dapat direplikasi pada konteks pendidikan vokasional lainnya.
Sentiment Analysis of Hotel Reviews in Senggigi using Decision Tree and Support Vector Machine Algorithm Putra, Lalu Muhammad Reza Suganda; Wijayanto, Heri; Wedashwara, I Gede Putu Wirarama
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 1 (2026): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i1.34960

Abstract

The tourism industry is a rapidly growing sector that significantly contributes to the economy, including Indonesia. One of the popular tourist destinations in Indonesia is Senggigi, located on the island of Lombok. This destination offers high natural and cultural appeal. In the tourism industry, hotels are crucial as primary accommodations for travelers to stay and rest. Tourist reviews on hotel services greatly influence potential visitor’s decisions in selecting the right accommodation. Therefore, sentiment analysis of hotel reviews is essential for understanding customer satisfaction levels and assisting hotel managers in improving service quality. This research applies a comparative quantitative approach using Decision Tree and Support Vector Machine (SVM) algorithms. The dataset consists of 6,920 hotel reviews collected from TripAdvisor platforms through web scraping techniques. Data preprocessing included data cleaning, case folding, tokenization, stop word removal, and stemming to enhance classification performance. Sentiment labels were categorized into positive, neutral, and negative classes. Model performance was evaluated using multiple metrics, including accuracy, precision, recall, and F1-score, to ensure a comprehensive assessment. The word frequency distribution reveals that that accommodation experience and room quality play a crucial role in customer satisfaction. Positive sentiment is dominated by adjectives like great, nice, and beautiful, reflecting pleasant experiences. Negative sentiment is expressed more politely through phrases such as not good or not very nice. Neutral sentiment tends to be descriptive without strong emotional expression. In terms of model performance, SVM outperformed the Decision Tree model, achieving an accuracy of 90%, precision of 91%, recall of 90%, and an F1-score of 85%. In comparison, the Decision Tree achieved an accuracy of 87%, precision of 84%, recall of 87%, and an F1-score of 85%. These findings demonstrate the superior capability of SVM in handling complex and diverse textual data. This study contributes academically by strengthening empirical evidence on the effectiveness of machine learning–based sentiment analysis in the tourism domain and practically by providing actionable insights for hotel managers to improve service quality and customer satisfaction.
Integration of Skyline Query with the PROMETHEE MCDM Method: A Case Study on Structural Official Selection Wijaya, Budiman; Wijayanto, Heri; Widiartha, Ida Bagus Ketut
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.29049

Abstract

The selection of structural officials within higher education institutions is a strategic and complex process that demands objectivity, transparency, and a data-driven approach. However, the increasing number of candidates and the diversity of evaluation criteria, such as years of service, rank, education, age, and performance, pose significant challenges in ensuring fair and efficient decision-making. Addressing this gap, this study proposes a hybrid method by integrating Skyline Query with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), offering a novel contribution to multi-criteria decision-making (MCDM) in public sector human resource selection. Skyline Query is employed as a preselection mechanism to eliminate 161 dominated candidates from an initial dataset of 228, allowing only the 67 most non-dominated candidates to advance to the ranking stage. PROMETHEE is then applied to generate rankings based on leaving and entering flow values. To evaluate the consistency and validity of this combined approach, the resulting rankings are compared with those from the pure PROMETHEE method using Spearman’s Rank Correlation. The analysis yields a high correlation coefficient of ρ = 0.967, indicating a very strong agreement between the two methods and confirming that the Skyline filtering does not distort ranking quality. The findings demonstrate that the Skyline+PROMETHEE integration significantly enhances the efficiency of the selection process by reducing computational complexity while preserving decision accuracy. Moreover, this approach strengthens the transparency and accountability of structural official selection, particularly in the context of the University of Mataram, and can be generalized to other institutional decision-making scenarios.
Estimation of Lithium-Ion Battery State of Charge: A Comparative Study of Four Variants of Kalman Filters Based on Equivalent Circuit Model Axiosa Nalendra Haidar Putra; Arya Kusumawardana; Heri Wijayanto; Satrio Prakoso
Emitor: Jurnal Teknik Elektro Vol 26, No 1: March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v26i1.15149

Abstract

State of Charge (SoC) adalah parameter kritis dalam Sistem Manajemen Baterai (BMS) lithium-ion, karena mempengaruhi keselamatan, kerahasiaan, dan efisiensi operasional. Studi ini membandingkan empat metode berbasis Filter Kalman, yaitu Extended Kalman Filter (EKF), Extended Kalman–Bucy Filter (EKBF), Unscented Kalman Filter (UKF), dan Unscented Kalman–Bucy Filter (UKBF), untuk estimasi SoC. Model baterai ekivalen Thevenin orde kedua diimplementasikan dalam MATLAB/Simulink menggunakan data eksperimental dari uji pelepasan arus konstan 1,5 A pada suhu 30 °C, 40 °C, dan 50 °C. Penghitungan Coulomb digunakan sebagai metode referensi, sementara akurasi estimasi dievaluasi menggunakan MAE, RMSE, dan MAPE. Hasil menunjukkan bahwa EKF dan EKBF dapat mengikuti tren SoC tetapi menghasilkan kesalahan yang lebih besar karena keterbatasan linearisasi. Sebaliknya, UKF dan terutama UKBF memberikan estimasi yang lebih akurat dan stabil. Oleh karena itu, UKBF dianggap sebagai metode yang andal untuk memperkirakan SoC dalam aplikasi BMS.
K-Means-Based Customer Segmentation with Domain-Specific Feature Engineering for Water Payment Arrears Management Akbar, Andi Hary; Wijayanto, Heri; Arimbawa, I Wayan Agus; Vynska Amalia Permadi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5186

Abstract

Indonesian water utilities face persistent challenges in managing payment delinquencies due to diverse customer characteristics, geographic limitations, and inadequate analytical capabilities. Addressing this issue is essential to optimizing revenue collection and supporting sustainable operations. This study aims to develop a data-driven customer segmentation framework using K-means clustering to enhance delinquency management. The framework incorporates six engineered features—Debt Efficiency, Payment Behavior Score, Category Risk Score, Geographic Risk Score, Consumption Intensity, and Financial Risk Score—designed to capture customer payment behavior, consumption patterns, and geographic risk. We applied the model to 1,500 anonymized customer records from PT Air Minum Giri Menang, focusing on those with delinquencies exceeding four months. Risk scoring was based on quintile distribution, and optimal clustering was determined through the elbow method combined with silhouette coefficient analysis. The results produced a two-cluster solution (silhouette score = 0.538), showing statistically significant differences across features (p ¡ 0.001) and medium-to-large effect sizes (Cohen’s d = 0.52–2.12). The segmentation identified medium-risk customers (86.7%) who require preventive management and high-risk customers (13.3%) who need billing intervention. Urban areas exhibited higher delinquency risk (18.4%) than rural areas (2.5%), indicating the need for geographically targeted strategies. All customer data was anonymized following Indonesian data protection protocols. In conclusion, the proposed framework transforms manual billing supervision into an adaptive, data-driven management system, contributing to segmentation research by introducing utility-specific engineered features for Indonesian water utilities.
Co-Authors Adhiguna, Lalu Mischa Khalqin Adi Santoso Afif, Muhammad Dzihnin Afwani, Royana Agitha, Nadiyasari Agus, I Made Agus Tresna Agustina Dwi Wijayanti Ahmad Musnansyah Ahmad Zafrullah Mardiansyah Aiyah Akbar, Andi Hary Alfalah*, Muhammad Fathi Alfalah, Muhammad Fathi Alip Sugianto Alip Sugianto, Alip Aliyah Aliyah Ardi, Komala Khairani Ari Hernawan Arief Sulistiyono Arifianto, Dinar Arya Kusumawardana Ashrisnaini, Yudhia Axiosa Nalendra Haidar Putra Baiq Rohiyatun Bimantari, Joselina Rizki Budi Irmawati Budiman Wijaya Budimansyah, Alif Laksana Cahya Damarjati Chamidah, Siti Chen, Hsing-Chung Chen, Yeong-Chin Deni Saputra Dewi Intan Kurnia Djuwitaningsih, Ekapti Wahjuni Eka Dwi Nurcahya, Eka Dwi Ellysabeth Usmiatiningsih Fadillah, Syifa Fadlurrahman, Firgi Febriyansyah, Benny Fitriani Fitriani Futaqi, Faruq Ahmad Garnika, Eneng Gilang, Aditia Hadi Sumarsono Haeni Budiati Hardyansah, Fitrah Huwae, Raphael Bianco I B K Widiartha I Gede Pasek Suta Wijaya I Wayan Agus Arimbawa, I Wayan Agus Ida Bagus K Widiartha Irma Putri Rahayu Jatmika, Andy Hidayat Jatmika, Andy Hidayat Khairani Ardi, Komala Kurrotaa'yun, Baiq Dwi Zulianti Kuska, Dila Ayu Ramanda Latifah, Nur Izza Luthfiyyah Az Zahro Ma'we, Hannatul Maharani, Sisilia Nabilla Mahendra Putra Raharja Mardiansyah, Ahmad Zafrullah Maulana, Mochamad Rafli Moh. Ali Wisudawan Prakara Moh. Ali Wisudawan Prakarsa S Muh. Ibnan Syarif, Muh. Ibnan Muhammad Ari Rifqi Munaji, Munaji Murpratiwi, Santi Ika Nabilla Maharani, Sisilia Naning Kristiyana, Naning Nanto, Abdian Desri Noor Alamsyah Octariana, Ghina Briliana Fatin Praseba, Diki Purnomo, Rochmat Aldy Putra, I Gede Darmawan Adi Pratama Putra, Lalu Muhammad Reza Suganda Rachmadia, Rizki Rahman, Pradita Dwi Ramadhani, Rizky Insania Rayani, Dewi Reyhan Adiba, Rahmat Rhesma Intan Vidyastari Rian Maulidani, Ahmad Riyanto, Didik Rizky, Dimas Maulana Rohmayani, Laeli Rosika, Herliana Saputra, Asep Rokhyadi Permana Saputri, Meilan Yulia Satrio Prakoso Setyo Budhi Sideman, Ida SRI ANGGRAINI Sri Hartono, Sri Sritrisniawati, Shella Elly Sunarto Sunarto Sunneng Sandino Berutu Suparjo Tajul Ma’arif, Muh Toha Ardi Nugraha Trinanda Putra, Karisma Tulus Haryono, Tulus Utami, Wiwid Vandha Pradwiyasma Widartha Vynska Amalia Permadi Wedashwara, I Gede Putu Wirarama Wedashwara, Wirarama Wesdawara, Wirarama Widanta, I Putu Widayanto, Andhi Widia Lingga, Elza Widodo, Agung Mulyo Widowati Siswomihardjo Widya Oktary Setiawardhani Widyani, Aprilian Widyani, Aprillian Wijaya, Budiman Wirarama Wedashwara Wirawan, I Gde Putu Wirarama Wedashwara Wisnujati, Andika Witarsana, I Nengah Dwi Putra Zahra, Dinda Zahrani, Nurul Qalbi Zubaidi, Ariyan Zulfah Hasanah