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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) dCartesian: Jurnal Matematika dan Aplikasi Jurnal Sistem Komputer Proceedings of KNASTIK TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Penelitian Pendidikan IPA (JPPIPA) CogITo Smart Journal INOVTEK Polbeng - Seri Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Sebatik Jurnal ULTIMA InfoSys MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Aptisi Transactions on Technopreneurship (ATT) Building of Informatics, Technology and Science FINANCIAL : JURNAL AKUNTANSI Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Aiti: Jurnal Teknologi Informasi Journal of Management and Bussines (JOMB) Jurnal Teknik Informatika (JUTIF) Advance Sustainable Science, Engineering and Technology (ASSET) International Journal of Social Science Indexia J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknologi Sistem Informasi Jurnal Algoritma Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Ilmiah Sains Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Eduvest - Journal of Universal Studies Midang Jurnal INFOTEL Journal of Technology Informatics and Engineering Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Scientific Journal of Informatics CSRID Advance Sustainable Science, Engineering and Technology (ASSET) INOVTEK Polbeng - Seri Informatika Jurnal DIMASTIK Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies
Hendry
Universitas Kristen Satya Wacana
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Penerapan Algoritma Support Vector Machine untuk Mendeteksi Uja-ran Kebencian dalam Media Sosial Twitter Shallom, Karsten Jonatthan; Hendry, Hendry
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7553

Abstract

Penelitian ini mengeksplorasi penerapan algoritma Support Vector Machine untuk mendeteksi ujaran kebencian di platform media sosial Twitter, khususnya dalam konteks bahasa Indonesia. Dengan lebih dari 330 juta pengguna, Twitter menjadi sarana yang rentan terhadap penyebaran ujaran kebencian yang dapat menimbulkan dampak negatif. Tujuan utama dari penelitian ini adalah mengembangkan sistem otomatis yang mampu mengidentifikasi ujaran kebencian secara efektif. Dataset yang digunakan terdiri dari 1564 tweet berbahasa Indonesia yang diambil dari isu politik pada tahun 2021. Proses analisis meliputi langkah-langkah seperti tokenisasi, stemming, dan penandaan kelas kata, diikuti dengan klasifikasi menggunakan SVM. Hasil penelitian menunjukkan bahwa 92.8% dari tweet yang dianalisis termasuk dalam kategori "no hate speech," sementara 7.2% teridentifikasi sebagai "hate speech." Model SVM menunjukkan performa yang sangat baik dengan akurasi mencapai 97.19%, recall 97.19%, presisi 97.28%, dan F1 Score 96.82%, tanpa adanya False Negatives. Penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam menciptakan lingkungan online yang lebih aman dan positif, serta meningkatkan pemahaman tentang karakteristik bahasa Indonesia dalam konteks deteksi ujaran kebencian.
Application of the PIECES Framework Method in E-Report Evaluation Muhammad Sholikin; Eko Sediyono; Hendry Hendry
Jurnal Penelitian Pendidikan IPA Vol 9 No SpecialIssue (2023): UNRAM journals and research based on science education, science applic
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9iSpecialIssue.6028

Abstract

The evaluation of the E-report application at SMK N 1 Banyudono is integral for assessing its alignment with the latest curriculum guidelines and its effectiveness in meeting students' learning and assessment needs. This evaluation ensures that learning support technologies, such as E-report, stay relevant, responsive to user needs, and supportive of the school's long-term educational objectives. Using the PIECES method, the assessment of user satisfaction comprehensively considers Performance, Information, Economy, Control, Efficiency, and Services indicators. The Likert scale aids in gauging user satisfaction across various aspects. Quantitative data analysis involves 26 respondents, including teachers, homeroom teachers, curriculum staff, and school principals, providing a holistic perspective on learning and assessments management. The PIECES method and Likert scale evaluation reveal a satisfaction level of 4.52, categorizing it as "SATISFIED." This comprehensive assessment covers system performance, information accuracy, cost-effectiveness, system control ease, time efficiency, and service flexibility. The qualitative descriptive approach using the PIECES method and Likert scale identifies areas for improvement, ensuring the E-report application remains efficient, relevant, and aligned with DITJEN VOKASI guidelines
Prediksi kebangkrutan perusahaan menggunakan metode klasifikasi: Studi kasus pada industri Ibrahim Ibrahim; Hendry Hendry
AITI Vol 23 No 2 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i2.305-318

Abstract

Prediksi kebangkrutan perusahaan merupakan aspek penting dalam bidang keuangan karena dapat memberikan dampak signifikan terhadap investor, kreditur, manajemen perusahaan, serta pemangku kepentingan lainnya dalam pengambilan keputusan strategis. Penelitian ini bertujuan untuk mengembangkan model prediksi kebangkrutan perusahaan yang akurat menggunakan algoritma XGBoost yang dioptimalkan melalui proses penyesuaian hiperparameter (hyperparameter tuning). Selain itu, penelitian ini juga menerapkan metode Synthetic Minority Over-sampling Technique (SMOTE) untuk mengatasi tantangan ketidakseimbangan data dengan meningkatkan representasi kelas minoritas. Model yang dikembangkan diuji pada dua dataset berskala besar (Taiwan dan US) untuk mengevaluasi konsistensi kinerja serta kemampuan generalisasi model. Hasil penelitian menunjukkan bahwa model XGBoost dengan hyperparameter tuning mampu menghasilkan performa terbaik dengan tingkat akurasi sebesar 98,94%, precision sebesar 0,98, dan recall sebesar 1,0. Selain itu, hasil pengujian menunjukkan bahwa model tersebut memiliki kinerja yang konsisten tanpa indikasi overfitting. Hasil pendekatan ini membuktikan bahwa XGBoost dengan penyesuaian hiperparameter mampu memberikan prediksi kebangkrutan yang akurat, konsisten, dan dapat diandalkan untuk diterapkan dalam berbagai konteks industri serta pada skala penggunaan yang lebih luas.
Analisis Tingkat Kepuasan Siswa terhadap Program Makan Bergizi Gratis di SMA Negeri 1 Manokwari dengan Pendekatan Mining Titin Restiani Mendrofa; Hendry Hendry
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 2 (2026): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i2.1842

Abstract

The Free Nutritious Meal Program (MBG) aims to improve students' nutritional status and learning quality; therefore, a data-driven evaluation is required to assess its implementation effectiveness. This study seeks to analyze the influence of program implementation on student satisfaction and to identify the resulting satisfaction segments. The research employed a descriptive quantitative approach involving 499 respondents. Data were collected through a Likert-scale questionnaire covering variables of program implementation and student satisfaction. The analysis utilized MANOVA, K-Means Clustering, and one-way ANOVA. The MANOVA results indicated that all implementation variables significantly affected student satisfaction (Sig < 0.001), with compliance to nutritional standards emerging as the most dominant factor (Wilks' Lambda = 0.805; F = 23.634). The K-Means analysis produced three satisfaction clusters: high (314 students), moderate (172 students), and low (13 students). The ANOVA test confirmed significant differences among clusters (Sig < 0.001), with the availability of healthy food (F = 145.428) as the main distinguishing factor. The findings of this study indicate that food variety and the availability of healthy meals are the main factors distinguishing students' satisfaction levels; therefore, the MBG program should focus on improving these two aspects to enhance overall student satisfaction.
Peningkatan Knowledge Capture dan Knowledge Sharing dalam KMS Tools dengan Kaizen Form Faisal Hakim Amrullah; Hendry; Irwan Sembiring
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.23-1.3409

Abstract

This study discusses the improvement of knowledge capture and knowledge sharing through the strengthening of a Kaizen-based Knowledge Management System (KMS) in the footwear manufacturing industry. The main problems include the suboptimal management of tacit knowledge and the limitations of document search based on simple keywords. This study applies an information retrieval method using TF-IDF and Cosine Similarity on 800 validated Kaizen documents through preprocessing, weighting, and document similarity measurement stages. The test results show that the proposed method performs better than conventional keyword-based search, with a precision value of 0.60, recall of 0.75, and F1-score of 0.67. The contribution of this study lies in the application of information retrieval methods to improve the effectiveness of knowledge retrieval in a Kaizen-based KMS, thereby supporting continuous improvement and organizational learning.
Pengaruh Kompetensi, Budaya Organisasi dan Lingkungan Kerja terhadap Kepuasan Kerja Karyawan pada Glory Ocean Lines Medan Hutapea, Jonathan Herman; Hendry, Hendry; Johan, Johan; Antar Eli Gulo; Michael Chandra; Efandri Agustian
Journal of Management and Bussines (JOMB) Vol. 8 No. 3 (2026): Journal of Management and Bussines (JOMB)
Publisher : IPM2KPE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/feezrc23

Abstract

This study aims to analyze the influence of competency, organizational culture, and work environment on employee job satisfaction at PT. Glory Ocean Lines Medan, both partially and simultaneously. The research method used is quantitative research with an explanatory research approach. The study population was 61 employees, and the entire population was used as the research sample. Data collection was conducted through questionnaires, interviews, and documentation. While data analysis used multiple linear regression analysis assisted by the SPSS program version 26, which includes the coefficient of determination test, F test, and t test. The results showed that competency has a positive and significant effect on employee job satisfaction with a calculated t value of 3.247 and a significance of 0.002, organizational culture has a positive and significant effect on employee job satisfaction with a calculated t value of 2.269 and a significance of 0.027, and the work environment has a positive and significant effect on employee job satisfaction with a calculated t value of 2.681 and a significance of 0.010. Simultaneously, competency, organizational culture, and the work environment have a positive and significant effect on employee job satisfaction, with a calculated F-value of 20.165 and a significance level of 0.000. The Adjusted R-Square value of 0.489 indicates that competency, organizational culture, and the work environment explain 48.9% of the variation in employee job satisfaction, while well-being is influenced by other factors outside the study, accounting for 51.1%. The conclusion of this study indicates that improving competency, organizational culture, and a conducive work environment can improve employee job satisfaction at PT. Glory Ocean Lines Medan.  Keywords: Organizational Culture, Job Satisfaction, Work Environment, Human Resources
Decision support system in machine learning models for a face recognition-based attendance system Joseph Teguh Santoso; Danny Manongga; Hendry Hendry
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.26412

Abstract

This research aims to develop a predictive model using face recognition-based attendance data and integrating decision support system (DSS) theory with machine learning (ML) techniques to identify high-performing teachers at vocational high schools (SMKs). The novelty of this research lies in integrating theory with the use of face recognition data and ML algorithms to predict and identify high-performing teachers, thereby enhancing decision-making processes and teacher performance management in SMK schools. The dataset consists of SMK teachers' attendance data obtained through a face recognition attendance system, totaling 998 entries. This research employs sensitivity analysis concepts from DSS theory and classification approaches from ML models utilizing support vector machine (SVM), decision trees (DT), and random forest (RF). The models are trained and tested on Google Colab using Python, with data distribution guided by the Pareto principle. The research findings indicate that integrating DSS theory with ML contributes to innovation and benefits in improving decision-making and teacher performance management by successfully predicting high-performing teachers. Evaluation results show the highest accuracy rate of 98% with the RF model, making it the best predictive model compared to the other two models.
Comparative Study of Classical and Quantum Machine Learning Models: Insights into Quantum Advantage in Materials Informatics Aris Tri Joko Harjanto; Hindriyanto Dwi Purnomo; Hendry
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2733

Abstract

Quantum Machine Learning (QML) has emerged as a promising paradigm for addressing increasing computational and representational demands in materials informatics. While classical models such as Support Vector Machines (SVM) achieve strong predictive performance, they often struggle to capture complex, highly correlated interactions in high-dimensional materials data. QML addresses this challenge by leveraging quantum-mechanical principles to construct expressive feature embeddings, where prospective quantum advantage lies in generating feature spaces that are difficult to approximate classically. In this study, 1,000 crystalline compounds from the Open Quantum Materials Database (OQMD) are evaluated in a binary classification task based on formation-energy stability. The dataset is normalized, reduced to four dimensions via Principal Component Analysis (PCA), and encoded into quantum circuits. Three QML models—QSVM, VQC, and QNN—are benchmarked against a classical SVM using repeated stratified evaluation. Results show that the classical SVM achieves the highest accuracy (91.8% ± 0.012), followed by QSVM (60.8% ± 0.035), while VQC and QNN perform significantly worse. This gap is driven by limited qubit capacity, encoding inefficiencies, restricted circuit expressivity, and optimization challenges. Nevertheless, QSVM demonstrates stable performance, suggesting that potential quantum advantage may emerge from improved feature encoding and kernel design rather than deeper variational circuits.
Penggunaan Data Mining dalam Mengklasifikasi Nominal Uang Rupiah dengan Metode Convolutional Neural Network (CNN) Suvirocana Suvirocana; Hendry Hendry
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 7 No. 1 (2026): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v7i1.1672

Abstract

This study aims to implement the Convolutional Neural Network (CNN) method to identify the authenticity of Indonesian banknotes through image classification into seven categories (classes). The dataset used consists of banknote images captured under various real-world conditions, including differences in printing quality, degrees of wear or deterioration, lighting variations, and multiple shooting angles to obtain diverse image variations. Through this approach, the system is expected to learn distinctive visual patterns and features of each banknote denomination, including texture, color, and embedded security elements found in genuine currency. After the training process, the model is evaluated to measure its accuracy and frames per second (FPS) as performance indicators for real-time recognition. The results of this research are expected to contribute to the development of effective and efficient image processing technology to assist in the automatic classification and detection of Indonesian banknote authenticity, thereby minimizing human error and enhancing security in financial transactions.
Analysis of the Performance Quality of the Information System and Information Technology of the Shopee Application Using Cobit 2019 Kevin Fransisco; Hendry
Journal of Innovation and Technology Polbeng Series on Informatics (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/c7ne4c42

Abstract

This research analyzes the quality of information system and information technology performance in the Shopee application using the COBIT 2019 framework. This research uses quantitative methods by collecting Likert scale-based questionnaire data, which was distributed to 112 respondents from Satya Wacana Christian University students class of 2020-2023. The technical analysis includes validity tests, reliability tests, t-tests, and ANOVA, with the |aim of determining the rel| relationship between gender |and cl|ass v|ari|ables on user s|atisf|action. The rese|arch results show th|at aspects of responsiveness and empathy h|ave |a signific|ant influence on user s|atisf|action, with a value of t = 1.685 (men) and t = 1.698 (women), as well as a p-value > 0.05 which shows there is no signific|ant difference based on gender. The 2019 COBIT Evaluation shows weaknesses in IT risk management, and optimization of IT resources, as well as the need to improve data security and customer service systems, and optimization of IT infrastructure. This research contributes to understanding the application of COBIT 2019 in IT govern|ance in e-commerce and provides recommendations for improvements for Shopee in improving user experience. The limit|ation of this research is | that the s| sample is limited to one institution and a certain period, so the results cannot be widely generalized.
Co-Authors Ade Iriani Adenia Kusuma Dayanthi Adhe Ronny Julians Adriyanto Juliastomo Gundo Agista Nindy Yuliarina Aldi Lasso Antar Eli Gulo Anton Hermawan Anton Hermawan Anugerah Widi April Lia Hananto Aris Tri Joko Harjanto Atik Setyanti, Angela Aviv Yuniar Rahman Baihaqi, Kiki Ahmad Bayangkariwati Tacoh, Yuliana Tien Benedictus Lanang Ido Hernanto Chandra, Dian W. Christine Dewi Dahnil Anzar Simanjuntak Daniel D. Kameo Danny Manongga Darmawan Utomo Darwin Lie Dewasasmita, Elsha Yuandini Dewi Puspitasari Efandri Agustian Eko Sediyono eric secada purba Erick Alfons Lisangan Erits Talapessy Erwien Christianto Ester Caroline Dwi Wijaya Wijaya Faisal Hakim Amrullah Fauzi Ahmad Muda Franly Salmon Pattiiha Fredryc Joshua Pa&#039;o Fredryc Joshua Pa'o Giarti, Giarti Gunawan, Ricardho Handoko, Andrew C Hanita Yulia Hendra Waskita Herdin Yohnes Madawara Hindriyanto Dwi Purnomo Huda, Baenil Hutapea, Jonathan Herman Ibrahim Ibrahim Irwan Sembiring Ismael Ismael Ivan Sukma Hanindria Ivanna K. Timotius Iwan Setiawan Iwan Setyawan Jessica Margaret Br Sembiring Johan Johan Joko Siswanto Joseph Teguh Santoso Julians, Adhe Ronny Juliastomo Gundo, Adriyanto Kesumawati, Ramadini Kevin Fransisco Kevin Fransisco Kho, Delvian Christoper Krismiyati Kristoko Dwi Hartomo Kurniawan Teguh Martono Leni Marlina Lidia Gayatri Madawara, Herdin Yohnes Mado, Priscianus Mikael Kia Magda Kitty Hartono Mahulete, Ebenhaezer Yohanes Abdeel Margaretha Intan Pratiwi Hant Martaliana Putri Agustina Merryana Lestari Michael Chandra Muhammad Rizky Pribadi Muhammad Sholikin Nadia Sofie Soraya Nalbraint Wattimena Nansy Stephanie Mongi Nugraha, Febrina Tesalonika Panja, Eben Paryono, Tukino Pratama Siregar, Hari Nanda Pratama, Arya Damar Purnomo, Hendryanto Dwi Ramos Somya Ravensca Matatula Ravensca Matatula Reinhard Alfaries Saemani Richard V. Llewelyn Robertus Bagaskara Radite Putra Rostina, Cut Fitri Rung Ching Chen Santoso, Joseph Teguh Saputri, Adelliya Dewi Septhiani, Angeline Setyanti, Angela Atik Shallom, Karsten Jonatthan Suharyadi Suherman, Suherman Sutarto Wijono Suvirocana Suvirocana Syefudin Syefudin Teddy Marcus Zakaria Thea Thiranadya Mardita Bulamey Theophilus Wellem Theopillus J. H. Wellem Titin Restiani Mendrofa Tukino, Tukino Uly, Novem Untung Rahardja Wahyuningsih, Novia Wibowo, Kurniawan Indra Winny purbaratri Winsy C.D Weku Yessica Nataliani