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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi POSITIF Edu Komputika Journal Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Computatio : Journal of Computer Science and Information Systems RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Khatulistiwa Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri JTERA (Jurnal Teknologi Rekayasa) Jurnal Sains dan Informatika INOVTEK Polbeng - Seri Informatika Matrix : Jurnal Manajemen Teknologi dan Informatika SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) KACANEGARA Jurnal Pengabdian pada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Indonesian Journal of Applied Informatics KOMPUTIKA - Jurnal Sistem Komputer KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terapan Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika JISICOM (Journal of Information System, Infomatics and Computing) EVOLUSI : Jurnal Sains dan Manajemen Building of Informatics, Technology and Science JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknologi Informasi dan Multimedia Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) JISA (Jurnal Informatika dan Sains) International Journal of Engineering, Technology and Natural Sciences (IJETS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Jurnal Digit : Digital of Information Technology Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Science in Information Technology Letters Journal of Soft Computing Exploration Jurnal Indonesia : Manajemen Informatika dan Komunikasi Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer International Journal Software Engineering and Computer Science (IJSECS) Jurnal Sains dan Teknologi International Journal Science and Technology (IJST) Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Scientific Research, Education, and Technology Jikom: Jurnal Informatika dan Komputer Journal of Data Science Theory and Application NERO (Networking Engineering Research Operation) SmartComp Jurnal Ilmu Komputer dan Sistem Informasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi Emitor: Jurnal Teknik Elektro IJISCS (International Journal of Information System and Computer Science)
Donny Avianto
Universitas Teknologi Yogyakarta
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Analisis Pengaruh Preprocessing Data dan Hyperparameter Tuning pada Backpropagation Neural Network dalam Klasifikasi Stroke Gunawan, Asrul; Hermawan, Arief; Avianto, Donny
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.956

Abstract

Data imbalance and scale differences between features are often the main factors that reduce the performance of neural network-based classification models. This study aims to analyze the effect of data preprocessing and hyperparameter tuning on the performance of Backpropagation Neural Network (BPNN) in stroke classification. This study used a stroke dataset from the Kaggle platform consisting of 5,110 patient data with 10 clinical features. The evaluation was conducted using five schemes and consisted of several data balancing techniques. These techniques include no balancing, SMOTE, and ADASYN. In addition, the evaluation also involved data normalization including no normalization, MinMaxScaler, and Z-Score. The BPNN model used has an architecture of 19 input neurons, 29 neurons in the hidden layer, and 1 output neuron. Hyperparameter tuning was performed by finding the best learning rate and number of epochs. The evaluation results showed that the model in scheme one has limitations. This limitation is most visible in identifying stroke classes. The application of SMOTE and MinMaxScaler in scheme two proved that the results were better and its performance increased significantly. On the other hand, the combination of ADASYN and Z-Score in scheme three showed more stable performance and was able to detect stroke cases more accurately. The hyperparameter tuning process in schemes four and five also proved to improve performance. The best results were obtained in scheme five, with an accuracy of 96.47%, a precision of 97.34%, a recall of 95.62%, and an F1-score of 96.47%. These findings indicate that the combination of adaptive balancing techniques, distribution-based normalization, and optimal parameter tuning is very effective in improving the accuracy and stability of BPNN for stroke classification.
Implementasi Speech Recognition Menggunakan Long Short-Term Memory untuk Software Presentasi Satriya Adhitama; Donny Avianto
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.6950

Abstract

Presentation is one of the methods for delivering thoughts, ideas, and concepts to an audience verbally. Presentation activities can be supported by presentation software that can be used to organize the sequence of material to be presented with visually appealing visuals. Operating presentation software requires technical assistance such as a remote, mouse, keyboard, and even a personal assistant, which can be distracting to the presenter as it limits their freedom in delivering the material. This distraction can be addressed through the implementation of speech recognition as a command to operate presentation software, making it easier for the presenter. A speech recognition system is developed using Long Short-Term Memory (LSTM), which can handle the issues of long-term dependency and vanishing gradient associated with Recurrent Neural Networks (RNN). There are 10 command words used to operate the presentation software. LSTM demonstrates superior performance when compared to alternative techniques like DNN, CNN, and SimpleRNN, achieving a training accuracy of 96.5%, a validation accuracy of 94.8%, and a testing accuracy of 94%. The LSTM method can be effectively used for sequential data to recognize real-time speech.
Penggunaan Metode DTW Pada K-Means Dalam Menganalisis Tren Penjualan Produk Laode Izat Trianto Haradin; Arief Hermawan; Donny Avianto
Jurnal Informatika dan Komputer Vol 16 No 1 (2026): April
Publisher : Sekolah Tinggi Ilmu Komputer PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Data is a collection of raw information that can be in the form of symbols, numbers, or words. Supermarkets are a type of modern market that functions as an intermediary between producers and consumers. Along with the increasing convenience of services and payment systems, sales transaction volumes have also increased. Based on this, this study proposes the use of the K-Means algorithm combined with Dynamic Time Warping (DTW) to cluster sales trend patterns. The main purpose of using DTW is to enable the comparison of sales time series that have shifting patterns, thus resulting in a more representative clustering process. The results of the clustering evaluation show that the K-Means configuration with the number of clusters K = 3 produces a Davies-Bouldin Index (DBI) value of 3.119, which indicates a relatively good level of cluster separation and compactness. This finding has important significance because it shows that the DTW-based K-Means approach is able to reveal meaningful sales trend patterns and can be used as a basis for strategic decision-making, such as stock planning, promotions, and more optimal supermarket sales management. Thus, the results of this study imply that the DTW-based K-Means approach can be used as an alternative method for analyzing sales patterns in the retail sector. These findings are expected to assist supermarket management in understanding sales behavior, supporting strategic decision-making, and improving the effectiveness of future inventory and promotional planning.
Optimization of Hyperparameter K in K-Nearest Neighbor Using Particle Swarm Optimization Muhammad Rizki; Arief Hermawan; Donny Avianto
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.20688

Abstract

This study aims to enhance the performance of the K-Nearest Neighbors (KNN) algorithm by optimizing the hyperparameter K using the Particle Swarm Optimization (PSO) algorithm. In contrast to prior research, which typically focuses on a single dataset, this study seeks to demonstrate that PSO can effectively optimize KNN hyperparameters across diverse datasets. Three datasets from different domains are utilized: Iris, Wine, and Breast Cancer, each featuring distinct classification types and classes. Furthermore, this research endeavors to establish that PSO can operate optimally with both Manhattan and Euclidean distance metrics. Prior to optimization, experiments with default K values (3, 5, and 7) were conducted to observe KNN behavior on each dataset. Initial results reveal stable accuracy in the iris dataset, while the wine and breast cancer datasets exhibit a decrease in accuracy at K=3, attributed to attribute complexity. The hyperparameter K optimization process with PSO yields a significant increase in accuracy, particularly in the wine dataset, where accuracy improves by 6.28% with the Manhattan matrix. The enhanced accuracy in the optimized KNN algorithm demonstrates the effectiveness of PSO in overcoming KNN constraints. Although the accuracy increase for the iris dataset is not as pronounced, this research provides insight that optimizing the hyperparameter K can yield positive results, even for datasets with initially good performance. A recommendation for future research is to conduct similar experiments with different algorithms, such as Support Vector Machine or Random Forest, to further evaluate PSO's ability to optimize the iris, wine, and breast cancer datasets.
ANALISIS KLASIFIKASI KEPUASAN PELANGGAN TERHADAP PELAYANAN CUSTOMER SERVICE UNTUK PENINGKATAN LAYANAN MENGGUNAKAN DATA MINING DENGAN DECISION TREE Lidya Nurmala Eva; Arief Hermawan; Donny Avianto
Journal of Information System, Informatics and Computing Vol 10 No 1 (2026): JISICOM (June 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v10i1.2404

Abstract

This study analyzes customer satisfaction with customer service using data mining techniques and the Decision Tree algorithm. The data was obtained from customer questionnaires completed after transactions and were processed through pre-processing, attribute labeling, and missing value handling. The dataset was split into 80% training data and 20% testing data to build and evaluate a classification model with two target categories: satisfied and dissatisfied. The modeling results show that the Consideration Label is the most dominant factor in determining customer satisfaction, while the Suggestion Label serves as a supporting attribute. Model evaluation produced an accuracy of 58%, with precision, recall, and F1-score for the dissatisfied class of 0.62, 0.66, and 0.64, respectively, and for the satisfied class of 0.53, 0.49, and 0.51, respectively. Based on these results, the Decision Tree method can be used to classify customer satisfaction, although further improvement in model performance is still needed to obtain more optimal predictions.
Segmentation-Aware Recommendation with Cluster-Specific Item Graphs Using Pointwise Mutual Information for Market Basket Analysis Khalifatur Rauf; Arief Hermawan; Donny Avianto
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9707

Abstract

Traditional Association Rule-based recommendation methods often exhibit limited coverage and high redundancy when applied to sparse transactional data, thereby constraining their effectiveness for product discovery in e-commerce systems. This study proposes a hybrid recommendation framework that integrates customer behavioral segmentation with graph-based item representation learning to address these limitations. Customers are first grouped into behaviorally homogeneous clusters using historical transaction features. For each cluster, an item co-occurrence graph is constructed and weighted using pointwise mutual information to mitigate sparsity bias and emphasize informative associations. Graph-based representation learning is then applied using Node2Vec to generate low-dimensional product embeddings that capture both local structural proximity and higher-order relational patterns. The proposed framework explicitly restricts the candidate item space to the Top 100 most frequent products within each behavioral cluster, thereby focusing the recommendation task on improving localized discovery within high-frequency product segments rather than global catalog exploration. The objective of this research is to assess whether segmentation-aware graph embeddings can outperform traditional FP-Growth association rules under a strict temporal split between the Historical Training Set and the Hold-out Evaluation Set, ensuring realistic and leakage-free evaluation. Model performance is evaluated using precision, recall, normalized discounted cumulative gain, and intra-list diversity on the Hold-out Evaluation Set. Experimental results indicate that the proposed graph-based approach improves ranking quality and diversity within constrained high-frequency item spaces, demonstrating more effective localized discovery within Top 100 product segments compared to FP-Growth. These results demonstrate that graph-based embeddings are more robust to sparse behavioral patterns within high-frequency product segments and better suited for exploratory recommendation scenarios within dense product subsets. The proposed framework offers a scalable and temporally valid foundation for knowledge-driven recommender systems.
Analisis Pengelompokan Tingkat Pemahaman Materi Siswa Berdasarkan Nilai Ujian Menggunakan Algoritma K-Means Cahaya Muzaddidah; Arief Hermawan; Donny Avianto
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 2 (2025): Mei 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i2.353

Abstract

Student understanding of learning content is an important indicator of educational success. This study aims to group student understanding based on their exam results in mathematics, English, science, social studies, and Arabic using the K-means Clustering algorithm. The data used consisted of 60 rows of student performance data, which were processed and standardized using Google Colab. The number of clusters was limited to two groups (K=2) to categorize students as having a high level of understanding or a basic level of understanding. The results showed that the K-means algorithm successfully identified groups of students with different levels of understanding based on their average exam scores. The group with a high level of understanding achieved an average score of more than 87.4. Teachers can use these Clustering results as a basis for developing more individualized and effective learning strategies for each group of students.
Pendekatan Hybrid: Naïve Bayes dan Decision Tree untuk Prediksi Kerusakan Mesin pada Industri Manufaktur PT X Aulia, Iin Rohmatika; Hermawan, Arief; Avianto, Donny
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.97905

Abstract

Abstrak : Perkembangan teknologi sistem informasi banyak dirasakan di setiap sector ekonomi. PT X merupakan perusahaan manufaktur di bidang percetakan, dimana produktivitas dipengaruhi dari efisiensi mesin. Optimasi produktivitas mesin dapat dilakukan dengan prediktif maintenance. Penelitian ini bertujuan untuk mengembangkan teknik data mining dalam prediktif kerusakan mesin produksi. Fokus utama penelitian adalah untuk mengklasifikasi kerusakan mesin berdasarkan data historis pada PT X. Model klasifikasi yang akan dikembangkan menggunakan algoritma model Naïve Bayes dan Decision Tree. Dalam klasifikasi ada 2 label keputusan yaitu tingkat resiko (tinggi, sedang rendah) dan kegiatan preventif (Ya,Tidak) Evaluasi dilakukan dengan menilai akurasi dan efektivitas setiap model. Hasil uji klasifikasi preventif dengan model Naïve Bayes memiliki nilai akurasi 97,90 %, sedangkan dengan model Decision Tree memiliki nilai akurasi 77%. Hasil uji klasifikasi tingkat resiko dengan model Naïve Bayes nilai akurasi 98% sedangkan dengan model Decision Tree nilai akurasinya 100%. hasil uji menunjukan untuk label preventif dengan 2 kelas lebih baik menggunakan model Naïve Bayes sedangkan label tingkat resiko dengan 3 kelas lebih baik menggunakan model Decision Tree. Hasil uji ini dapat dijadikan acuan Perusahaan X khususnya divisi maintenance dalam melakukan penjadwalan prediktif maintenance. Metode ini juga dapat diterapkan pada Perusahaan lain jika memiliki data historis kerusakan mesin, memiliki mesin dengan jenis operasional yang relevan, dan memiliki tujuan dan klasifikasi yang sesuai.===================================================Abstract :The advancement of information system technology has significantly impacted all economic sectors. PT X, a manufacturing company in the printing industry, experiences productivity fluctuations that are strongly influenced by machine efficiency. Optimizing machine productivity can be achieved through predictive maintenance. This study aims to develop data mining techniques for predicting machine failures in production. The primary focus is to classify machine failures based on historical data from PT X. The classification models employed are the Naïve Bayes algorithm and the Decision Tree algorithm. Two classification labels are used: risk level (high, medium, low) and preventive action (Yes, No). Evaluation was conducted by measuring the accuracy and effectiveness of each model. The classification results for the preventive action label showed that the Naïve Bayes model achieved an accuracy of 97.90%, while the Decision Tree model reached 77%. For the risk level label, the Naïve Bayes model achieved 98% accuracy, and the Decision Tree model achieved 100%. The findings indicate that the Naïve Bayes model is more suitable for binary classifications such as preventive actions, while the Decision Tree model performs better in multi-class classifications such as risk levels. These results can serve as a reference for PT X’s maintenance division in scheduling predictive maintenance. Moreover, the method can be applied to other companies, provided they have historical machine failure data, machines with similar operational characteristics, and compatible classification objectives
Co-Authors Adicahya, Bina Sukma Adityo Permana Wibowo Alwani, Adie G. Amalia Rizki Wulandari Apriansyah, Ferryma Arba Ardiansyah, Diky Aribowo Aribowo Arief Hermawan Arieska Restu Harpian Dwika Arif Hermawan, Arif Ashari, Nadia Aulia, Iin Rohmatika Aziz Perdana Baiq Nurul Azmi Bimantoro, Nazar Iqbal Bowo Hirwono Budiyanto, Irfan Cahaya Muzaddidah Dewi, Amelia Citra Dian Wijayanti Dimas Dwi Kurniawan Dwi Ratnawati, Dwi Edi Priyanto Enggar Novianto Enggar Novianto Erfin Nur Rohma Khakim Fadhila, Arifa Farras Fadilah, Faiz Fahri Putra Herlambang Fakharudin, Panji Rangga Adzan Fajar Faqih, Allan Bil Febiansyah Annaufal Ahnaf Fauzi Ferdinandus Edwin Penalun Gumilang, Muhammad Satrio Gunawan, Asrul Hanif, Rifqi Fadhlurrahman Hardiyantari, Oktavia Herdy Andriksen Ilmy Eka Handayani Imantoko Imantoko Indra Maulana Iqbal, Muhammad Izza Jagad Raya Ramadhan Khalifatur Rauf Kurniawan, Dimas Rizqi Kusumastuti, Asriana Dyah Laode Izat Trianto Haradin Lidya Nurmala Eva Maulana, Adha Muh Arifandi Muhammad Irsyad Indra Fata Muhammad Kusban Muhammad Rizki Muhammad Rizki Nasmah Nur Amiroh Novaldy, Olwin Kirab Nur Widiastuti Nurazila, Siti Octavianus, Yonathan Perdana, Aziz Purba, Yurjaa Ghoniyyan Purnomo Pratama, Rizki Putra, Kristianto Pratama Dessan Rahma Nur Azizah Reski Noviana Rian Oktafiani Rian Oktafiani Rianto Rianto Rizarta, Rusma Eko Fiddy Rizky Samudra Falasyfa Roy Fasti Rubangi Rubangi Rudi, Rudiono Rusma Eko Fiddy Rizarta Saputra, Candra Heru Satriya Adhitama Setiawan, Muhhamad Ajun Siti Rokhanah Soraya Fatmawati Sri Wulandari SRI WULANDARI Sutarman Sutarman Syafrudin, Teguh Syahab, Alfin Syarifuddin Teguh Syafrudin Tri Untoro, Iwan Hartadi Tri Widodo Vivianti Wahid, Ach. Nur Aqil Widyastuti, Evi