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All Journal International Journal of Electrical and Computer Engineering Techno.Com: Jurnal Teknologi Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika JURIKOM (Jurnal Riset Komputer) Jurnal Review Pendidikan dan Pengajaran (JRPP) JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Computer System and Informatics (JoSYC) International Journal of Advances in Data and Information Systems Jurnal Nasional Teknik Elektro dan Teknologi Informasi Servis : Jurnal Pengabdian dan Layanan kepada Masyarakat
Irwan Budi Santoso
Universitas Islam Negeri Maulana Malik Ibrahim, Malang
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Penerapan Metode Support Vector Machine (SVM) Dalam Klasifikasi Produktivitas Padi Hamim Tohari; Sri Harini; Muhammad Ainul Yaqin; Irwan Budi Santoso; Cahyo Crysdian
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4538

Abstract

Indonesia is one of the largest rice producing countries in the world. Almost 95% of the Indonesian population consumes rice as a mandatory staple food, so that every year the demand for rice increases along with the increase in population. East Java is known as the largest rice producing province in Indonesia. To optimize rice production, Central Java province can group rice producing cities or districts. This aims to see and find out cities or districts that have the potential to produce rice as well as find out areas that have less than optimal rice production. To see whether the need for rice in East Java province is met, it is necessary to predict rice productivity in the East Java region so that it can be used as a basis for efforts to increase rice yields for the next period. In this research, the Support Vector Machine (SVM) method was used to classify data for predicting crop yields in East Java province. The advantage of the SVM algorithm is that it can be used for classification and regression problems with linear kernels or non-linear kernels. The data used is agricultural statistical data obtained from the website jatim.bps.go.id. The data is then analyzed using a data mining process. The results of this research are in the form of a prediction pattern with a decision tree which can be used as a basis for predictions in estimating harvest results in the next period. From dividing 80% training data and 20% testing data, results were obtained with 80% accuracy when predicting category '0' (not on target), and 100% accuracy when predicting category '1' (on target). And overall, the classification model has an accuracy of 88%. The contribution to be achieved in this research is to provide ideas for data processing in the agricultural sector.
Deteksi Spam pada Trending Topik Twitter Berbahasa Indonesia Menggunakan Artificial Neural Network dan Stochastic Gradient Descent Heni Prasetyo; Cahyo Crysdian; Irwan Budi Santoso

Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/explorit.v15i2.4493

Abstract

Dalam beberapa tahun terakhir, jejaring sosial telah menjadi platform yang sangat populer di kalangan masyarakat. Twitter merupakan salah satu jejaring sosial berbasis micro-blogging yang populer digunakan. Twitter memiliki fitur trending topik yang menampilkan hashtag atau kata yang sedang ramai diperbincangkan. Twitter menyediakan Application Programming Interface untuk para developer untuk memudahkan developer mengakses informasi yang ada di program utama. Hal tersebut kemudian banyak disalah gunakan untuk memposting tweet spam dengan menumpang hashtag atau kata yang sedang menjadi trending topik. Dengan banyaknya spam tweet yang terdapat dalam trending topik Twitter menyebabkan informasi yang didapatkan pengguna menjadi tidak relevan. Oleh karena itu proses deteksi tweet spam sangat penting dilakukan sehingga pengguna bisa mendapatkan informasi yang relevan terkait dengan topik yang sedang menjadi trending. Dalam penelitian deteksi spam ini metode yang digunakan adalah metode Artificial Neural Network dan metode Artificial Neural Network dengan optimasi Stochastic Gradient Descent. Hasil penelitian menunjukkan metode Artificial Neural Network memiliki akurasi sebesar 70% dan metode Artificial Neural Network dengan optimasi Stochastic Gradient Descent memiliki akurasi sebesar 77%
Citra Tekstur Terbaik Untuk Gaussian Naïve Bayes Dengan Interpolasi Nearest Neighbor Irwan Budi Santoso; Shoffin Nahwa Utama; Supriyono
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 1: Februari 2024
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v13i1.8730

Abstract

One of the factors affecting the performance of the Gaussian naïve Bayes classifier (GNBC) in texture image classification is the image size (dimensions). Image size is one of the best texture image criteria besides its pixel value. In this study, a method is proposed to obtain the size of the best texture image for GNBC by nearest neighbor (NN) interpolation optimization. The best texture image size with interpolated pixel values makes GNBC able to distinguish texture images in each class with the highest performance. The first step of the proposed method was to determine the texture image size for training through a combination of row and column sizes in the optimization process. The next important step in generating the new texture images was resizing each of the original texture images using NN interpolation. The next step was to build GNBC based on the new image from interpolation and determine the classification accuracy. The last step was to select the best texture image size based on the largest classification accuracy value as the first criterion and image size as the second criterion. The evaluation of the proposed method was carried out using texture image data from the CVonline public dataset involving several test scenarios and interpolation methods. The test result shows that in scenarios involving five classes of texture images, GNBC with NN interpolation gives the smallest classification accuracy value of 89% and the largest 100% at the best image size, 14 × 32 and 47 × 42, respectively. In scenarios involving small to large class numbers, GNBC with NN interpolation provides classification accuracy of 81.6%–95%. From these results, GNBC with NN optimization gives better results than other nonadaptive interpolation methods (bilinear, bicubic, and Lanczos) and principal component analysis (PCA).
The Innovation for the Development of Ceria Park Tourism Objects in Warangan Village to Increase Local Tourist Attraction: Inovasi Pengembangan Objek Wisata Taman Ceria Di Desa Warangan Untuk Meningkatkan Daya Tarik Wisatawan Lokal Asep Sunarko; Ambiya, Sri winduri; Mar Atul Ma’rifah; Irwan Budi Santoso; Ihda Noor Mujib
Servis : Jurnal Pengabdian dan Layanan kepada Masyarakat Vol. 1 No. 2 (2023): Juni
Publisher : CV. Nature Creative Innovation

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

Abstract

To improve local tourist attraction, an innovative development of cheerful garden attractions in Warangan villages was undertaken. The development of those who seek to privatize aims to make those available in terms of quality tools and infrastructure, making destinations more accessible and desirable, as well as the character and income of those around them. The goal of the study was to create a cheerful garden tour in the Warangan village. The method employed is field research, which describes the development and research of cheerful-age tourist attractions in the village of Kepil district Warangan, and the second is to include a description of the drives and obstacles in the development of cheerful garden attractions. The recommended proposal that can be used to achieve the goal of developing tourist attractions in a fun park is to educate the tourist authority on the importance of preserving the urgent tourist attraction that impedes the expansion of tourism needed to accommodate the promotion specifically targeting investors and creating innovation that can boost tourist attraction.Key words: Innovation, development, and local tourist attraction
PEMETAAN SENTIMEN MASYARAKAT TERHADAP PILPRES 2024 DENGAN ALGORITMA SELF-ORGANIZING MAP Dwi Purbo Yuwono; Irwan Budi Santoso; Ririen Kusumawati
Jurnal Review Pendidikan dan Pengajaran Vol. 7 No. 3 (2024): Volume 7 No 3 Tahun 2024
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jrpp.v7i3.28830

Abstract

Pemilihan Umum (PEMILU) adalah salah satu cara untuk memilih presiden, kepala daerah, dan anggota parlemen yang berlangsung setiap lima tahun sekali. Dalam memasuki tahun- tahun politik saat ini akan banyak bertebaran informasi dan komentar dari masyarakat terhadap pelaksanaan pemilu, komentar atau pendapat yang disampaikan akan sangat beragam dimulai dari dukungan terhadap pelaksanaan pemilu, penggiringan opini publik, ujaran kebencian dan komentar-komentar lainnya. Kemajuan teknologi saat ini mengakibatkan penyampaian pendapat dapat dengan mudah dipublikasikan melalui media sosial, salah satunya adalah melalui media twitter, twitter menjadi salah satu media sosial yang paling sering digunakan masyarakat dalam mengemukakan pendapatnya karena dianggap bebas. Oleh karena itu, pada penelitian ini diusulakan pemataan sentimen atau opini masyarakat tentang Pilpres melalui X-Twitter, baik itu positif, negatif, atau netral dengan menggunakan Term Frequency-Inverse Document Frequency (TF-IDF) dan metode Self-Organizing Maps (SOM). Dari hasil penelitian didapatkan bahwa Algoritme TF-IDF dan Self-Organizing Maps (SOM) dengan sentimen cuitan pengguna Twitter dengan Hasil pengujian masing-masing model dengan menggunakan confusionmatrix didapatkan rata-rata accuracy sebesar 81%, precision 80,3%, recall 81%, dan f-measure 80%.
Enhancing Student Collaboration in Academic Projects Through a Content-Based Filtering Recommender System Anwar, Aldian Faizzul; Kusumawati, Ririen; Yaqin, M. Ainul; Santoso, Irwan Budi; Zuhri, Abdurrozaq Ashshiddiqi
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1329

Abstract

The Informatics Engineering Study Program at UIN Maulana Malik Ibrahim Malang facilitates students in developing their interests and talents through 10 academic communities that serve as forums for knowledge exchange and innovation in IT project development. However, a challenge arises in assigning suitable students to appropriate projects, resulting in many projects being completed by a limited set of students. To address this, a recommender system for academic project members was developed using the Content-Based Filtering method. This system assists project initiators in selecting competent team members based on students’ prior experiences, considering the similarity between project requirements and student profiles. A dataset of 198 student-completed projects was used, with preprocessing, TF-IDF, and cosine similarity applied in the recommendation process. The system was implemented using the Flask framework with Python and HTML. Evaluation was conducted using the SUS method for usability (achieving a score of 79, categorized as excellent) and MAP for model performance across three scenarios. Scenario one (random community) scored 0.92, scenario two (same community) scored 0.79, and scenario three (comparison with actual members) scored 0.98. The results indicate that broader search scopes yield more accurate recommendations. This research contributes to the improvement of collaborative IT project in academic environments by enabling data-driven student member selection. The proposed system has the potential to be adopted by other academic institutions facing similar team formation challenges.
Extractive Text Summarization Karya Ilmiah Mahasiswa Menggunakan Fuzzy C-Means dan Vector Space Model Cahyani, Vivin Octavia; Faisal, Muhammad; Santoso, Irwan Budi
Techno.Com Vol. 24 No. 2 (2025): Mei 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i2.12641

Abstract

Artikel jurnal ilmiah terus meningkat setiap tahunnya, sering kali mempersulit pembaca dalam menyaring informasi inti secara efisien. Informasi yang kurang efisien membuat pembaca harus membaca ulang artikel sehingga memerlukan banyak waktu. oleh karena itu, dibutuhkan sebuah alat untuk menemukan inti informasi dari artikel jurnal ilmiah secara cepat dan efisien. Untuk mengatasi masalah ini, peringkasan teks secara otomatis diperlukan, khususnya untuk artikel jurnal ilmiah berbahasa indonesia. Penelitian ini mengembangkan sistem peringkasan teks otomatis menggunakan metode Fuzzy C-Means dan Vector Space Model menggunakan pembobotan kalimat TF-IDF (Term Frequency Invers Document Frequency). Evaluasi sistem menggunakan metriks ROUGE-1 dan ROUGE-2. Hasil pengujian menunjukkan bahwa sistem terbaik, pada tingkat kompresi 30% serta menggunakan stemming memberikan hasil terbaik dan seimbang, dengan rata-rata ROUGE-1 Precission 0.5331, Recall 0.5034, F1-Score 0.4975 dan Accuracy 0.5183. Hasil penelitian ini menunjukkan bahwa model dengan stemming lebih disarankan untuk menghasilkan ringkasan yang lebih relevan dan akurat pada tingkat kompresi yang lebih tinggi.   Kata kunci: Fuzzy C-Means, Vector Space Model, Peringkasan Teks
Algoritma Random Forest dan Synthetic Minority Oversampling Technique (SMOTE) untuk Deteksi Diabetes Nurussakinah, Nurussakinah; Faisal, Muhammad; Santoso, Irwan Budi
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.221-234

Abstract

Diabetes is one of the challenges in global health. Indonesia ranks 5th in the world with the highest rate of diabetes. This research uses the Random Forest algorithm for diabetes detection. The purpose of the study is to detect diabetes with the Random Forest algorithm that provides accurate and efficient results in the early diagnosis of diabetic patients. The data used is secondary data "Diabetes Dataset" which consists of 952 data and has 17 features. The test scenario in this study divides the data consisting of 3 parts, namely scenario 1 90%:10% ratio, scenario 2 70%:30% ratio, scenario 3 50%:50% ratio. In each scenario, a comparison between using SMOTE and not using SMOTE is applied. The best performance results are obtained in scenario 1 which uses SMOTE, which produces 97% accuracy, 100% precision, 94% recall and the last is F1-Score which produces 97%.
Rainfall Prediction Using Attention-Based LSTM Architecture Romadhani, Ahmad; Santoso, Irwan Budi; Crysdian, Cahyo
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8727

Abstract

This study addresses the challenge of accurately predicting rainfall in regions with complex climate dynamics, such as Malang Regency, East Java. It evaluates the performance of a Long Short-Term Memory (LSTM) model enhanced with the Bahdanau Attention Mechanism, comparing it with a Standard LSTM model in forecasting daily rainfall based on historical weather parameters including average temperature, relative humidity, sunshine duration, and wind speed. Using daily data from BMKG covering 2000 to 2023, both models underwent a structured machine learning process including data preprocessing, feature selection, model training, and evaluation. The Attention-Based LSTM consistently outperformed the Standard LSTM, particularly in handling rainfall anomalies, achieving an MSE of 0.00800 and RMSE of 0.08948, compared to 0.00817 and 0.09039 respectively for the Standard LSTM. These results demonstrate that integrating Bahdanau Attention improves the model’s focus on relevant temporal features, enhancing prediction accuracy and robustness. The architecture consisting of two LSTM cells combined with the attention mechanism effectively captures complex sequential patterns that the standard model tends to overlook. This research highlights the potential of attention mechanisms in time series weather prediction, contributing to more reliable early warning systems, adaptive agricultural strategies, and disaster risk reduction frameworks. Future work could explore hybrid models or incorporate additional weather features to further improve performance and generalization.
Two-step majority voting of convolutional neural networks for brain tumor classification Santoso, Irwan Budi; Utama, Shoffin Nahwa; Supriyono, Supriyono
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4087-4098

Abstract

Brain tumor type classification is essential for determining further examinations. Convolutional neural network (CNN) model with magnetic resonance imaging (MRI) image input can improve brain tumor classification performance. However, due to the highly variable shape, size, and location of brain tumors, increasing the performance of tumor classification requires consideration of the results of several different CNN models. Therefore, we proposed a two-step majority voting (MV) on the results of several CNN models for tumor classification. The CNN models included InceptionV3, Xception, DensNet201, EfficientNetB3, and ResNet50; each was customized at the classification layer. The initial step of the method is transfer-learning for each CNN model. The next step is to carry out two steps of MV, namely MV on the three CNN model classification results at different training epochs and MV on the results of the first step. The performance evaluation of the proposed method used the Nickparvar dataset, which included MRI images of glioma, pituitary, no tumor, and meningioma. The test results showed that the proposed method obtained an accuracy of 99.69% with a precision and sensitivity average of 99.67% and a specificity of 99.90%. With these results, the proposed method is better than several other methods.
Co-Authors Ahmad Fahmi Karami Ainul Yaqin Ambiya, Sri winduri Anwar, Aldian Faizzul Asep Sunarko Cahyani, Vivin Octavia Cahyo Crysdian Cahyo Crysdian Cahyo Crysdian Cahyo Crysdian Diantoro, Aris Dwi Purbo Yuwono Eko Agus Moh. Iqbal Galib, Galan Ramadan Harya Hamim Tohari Heni Prasetyo Ihda Noor Mujib Ihsan Bagus Fahad Arafat Mar Atul Ma’rifah Moh. Heri Susanto Mokhamad Amin Hariyadi Muhammad Ainul Yaqin Muhammad Faisal Nurussakinah, Nurussakinah Purwanto, Sofi Dwi Ririen Kusumawati Romadhani, Ahmad Shoffin Nahwa Utama Shoffin Nahwa Utama Sri Harini Suhartono Supriyono Supriyono Supriyono Zuhri, Abdurrozaq Ashshiddiqi