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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer JPM (Jurnal Pemberdayaan Masyarakat) Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Jifosi Eduvest - Journal of Universal Studies Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika NAWASENA BHAKTI Jurnal Pengabdian Masyarakat (J-AbMas)
Ifnu Wisma Dwi Prastya
Universitas Nahdlatul Ulama Sunan Giri
Religion Aerospace Engineering Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Neuroscience Public Health Social Sciences Other

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Digitalisasi Tata Kelola Desa Kedungprimpen Melalui Aplikasi Sistem Administrasi Persuratan dan Inventaris Aset Mula Agung Barata; Ridlwan Hambali; Ifnu Wisma Dwi Prastya; Shofiatuz Zulfia; Teguh Pribadi
Jurnal Pemberdayaan Masyarakat Vol 11 No 1 (2026): Mei
Publisher : Direktorat Penelitian dan Pengabdian kepada Masyarakat (DPPM)

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Abstract

Kedungprimpen Village faces various administrative challenges due to the manual management of correspondence and asset inventory, resulting in duplicated letter numbers, delayed services, and inaccurate village asset data. This community service program aims to develop and implement SI-Desaku, an integrated web- and desktop-based information system designed to support village correspondence administration and asset management in a unified manner. The implementation method includes socialization, system requirements analysis, application development, field testing, technical training, intensive mentoring, and program evaluation. The results indicate that SI-Desaku successfully eliminated letter number duplication by 100%, reduced service time from 15–30 minutes to 5–10 minutes, and provided an accurate, real-time village asset database. Furthermore, the digital literacy of village officials improved significantly, as evidenced by 90% of participants being able to operate the system independently. The implementation of SI-Desaku contributes to the realization of transparent, accountable, and sustainable village governance, while also being oriented toward improving the quality of public services.
Perbandingan Algoritma C4.5 dan Random Forest dalam Klasifikasi Kekeringan Tembakau Berbasis Electronic Nose Rahmat Tegar Patriot Hari Lambang; Dimas Saputra; Muh. Mashdarul Hilmi Aufa; Ifnu Wisma Dwi Prastya
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10057

Abstract

Tobacco quality is influenced by changes in volatile compounds that occur during the drying process and can be represented through the sensor response patterns on the electronic nose (E-Nose) device. Various machine learning algorithms have been used to classify tobacco based on E-Nose data, but the performance of each algorithm can vary depending on the characteristics of the data used. Therefore, this study aims to analyze and compare the performance of the C4.5 and Random Forest algorithms in classifying tobacco quality based on volatile compound data obtained using the E-Nose. A total of 375 tobacco sample data representing four drying conditions were used in this study. The preprocessing stage was carried out using the Interquartile Range (IQR) method to remove outliers and Moving Average to reduce noise in the MQ-4, MQ-7, and MQ-135 sensor signals. Furthermore, both algorithms were evaluated using stratified 10-fold cross-validation to obtain stable performance estimates. The results indicated that Random Forest performed better than C4.5 in all evaluation metrics. Random Forest achieved an accuracy of 94.44%, a Cohen's Kappa value of 0.9259, an MCC of 0.9262, a balanced accuracy of 0.945, and a cross-entropy log loss of 0.4291. These results indicate that Random Forest is more effective in classifying tobacco quality based on volatile compound data obtained using E-Nose.
Comparison of Decision Tree Algorithms and Support Vector Machine (SVM) In Depression Classification In Students M. Khoirul Risqi; Ifnu Wisma Dwi Prastya; Muhammad Jauhar Vikri
Eduvest - Journal of Universal Studies Vol. 5 No. 4 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i4.51108

Abstract

Mental health in adolescents, especially students, is an important concern in the world of education. Early detection of symptoms of depression in students can help preventive efforts in handling them. This study aims to compare the performance of two classification algorithms, namely Decision Tree and Support Vector Machine (SVM) in detecting the level of depression in students based on data obtained from the Kaggle platform. The dataset used consisted of 502 student data with 10 features that caused depression and 1 target class. The research stage includes data preprocessing, which includes data cleaning, categorical value encoding, and normalization with the Min-Max Scaling method. The model was developed using the 5-Fold Cross Validation method to evaluate the classification performance of each algorithm. Model evaluation was carried out using precision, recall, and accuracy metrics. The test results showed that the SVM algorithm had better performance with a precision value of 93.63%, recall of 95.21%, accuracy of 94.22%, and F1-score of 94.68%. Meanwhile, Decision Tree obtained a precision of 81.77%, a recall of 84.90%, an accuracy of 82.86%, and an F1-score of 83.64%. Based on these results, it can be concluded that the Support Vector Machine is superior in classifying depression in students compared to Decision Tree
ANALISIS SENTIMEN TERHADAP PROGRAM KOPERASI DESA MERAH PUTIH DI MULTI PLATFORM MENGGUNAKAN METODE TF-IDF DAN SVM Lailatul Qodriyah; Ferdita Inayah Hestu Saputri; M. Ahsanul Fikri; Ifnu Wisma Dwi Prastya
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 1 (2026): JATI Vol. 10 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i1.17106

Abstract

Program Koperasi Desa Merah Putih merupakan kebijakan strategis pemerintah dalam meningkatkan perekonomian desa melalui koperasi berbasis gotong royong. Seiring berkembangnya media digital, masyarakat secara aktif menyampaikan opini terhadap program ini melalui berbagai platform, sehingga menghasilkan data teks tidak terstruktur yang sulit dianalisis secara manual dan belum menggambarkan persepsi publik secara objektif. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap Program Koperasi Desa Merah Putih secara multi-platform guna memperoleh gambaran opini publik yang komprehensif. Metode yang digunakan adalah text mining dengan ekstraksi fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF) dan klasifikasi sentimen menggunakan algoritma Support Vector Machine (SVM). Data komentar dikumpulkan dari platform X (Twitter) dan TikTok melalui teknik web scraping, kemudian dilakukan preprocessing, pelabelan sentimen, serta evaluasi model menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan adanya perbedaan karakteristik sentimen antar platform, di mana sentimen positif mendominasi pada platform X, sedangkan sentimen negatif lebih banyak ditemukan pada platform TikTok. Model SVM menghasilkan akurasi sebesar 98,73% pada platform X dan 94,32% pada platform TikTok, yang menunjukkan bahwa kombinasi TF-IDF dan SVM efektif dalam mengklasifikasikan sentimen masyarakat terhadap kebijakan publik
KLASIFIKASI PENYAKIT JANTUNG BERDASARKAN FAKTOR KLINIS MENGGUNAKAN ALGORITMA XG BOOST, RANDOM FOREST, SUPPORT VECTOR MACHINE (SVM) DAN K-NEAREST NEIGHBOR (KNN) Siti Azizah Aini; Diah Fitriani; Mohammad Alif Awwalin; Ahmad Abil Rizky Ramadhan; Ifnu Wisma Dwi Prastya
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 2 (2026): JATI Vol. 10 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i2.17644

Abstract

Saat ini penyakit jantung menjadi tantangan utama dalam bidang kesehatan masyarakat, diperlukan sarana deteksi dini yang efektif karena penyakit jantung adalah salah satu penyebab kematian paling umum di dunia. Metode mesin pembelajaran dikembangkan untuk membantu proses diagnosis karena data klinis yang kompleks dan metode konvensional yang terbatas. Menggunakan algoritma Random Forest, Support Vector Machine (SVM), XGBoost, dan K-Nearest Neighbors (KNN), penelitian ini bertujuan untuk mengklasifikasikan penyakit jantung berdasarkan faktor klinis. Dengan jumlah 1.888 dataset pasien dari  Kaggle. Proses preprocessing data, pembagian data latih, dan pembagian data uji adalah semua hasil dari penelitian ini, dengan rasio 80:20, pembuatan model dan penilaian menggunakan akurasi, presisi, recall, dan skor F1. Hasil pengujian menunjukkan bahwa Random Forest adalah yang terbaik dengan akurasi sebesar 97,88%, diikuti oleh XGBoost sebesar 94,71%, SVM sebesar 92,59%, dan KNN sebesar 90,21%. Berdasarkan hasil ini, Random Forest dianggap sebagai sistem yang paling ideal dan dapat dipercaya untuk mengklasifikasikan penyakit jantung berdasarkan faktor klinis, dan dapat digunakan sebagai sistem pendukung keputusan dalam bidang kesehatan.
Perbandingan Algoritma Machine Learning untuk Klasifikasi Kopi Menggunakan Data Sensor Electronic Nose dan Tongue Dwi Issadari Hastuti; Mula Agung Barata; Ifnu Wisma Dwi Prastya
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9349

Abstract

Coffee is a leading Indonesian commodity with a diversity of aromas and flavors influenced by variety and region of origin. However, the process of identifying and classifying coffee types is still often carried out conventionally through sensory testing, which is subjective, time-consuming, and dependent on panelist expertise. This situation encourages the need for a more objective and consistent automated approach based on sensor technology and machine learning. This study aims to compare the performance of several machine learning algorithms, namely Logistic Regression, Support Vector Classifier (SVC), and Random Forest, in classifying Indonesian coffee types using multisensor Electronic Nose and Electronic Tongue data. The data used comes from gas, temperature, and pH sensors with a total of 1,503 samples representing ten coffee classes. The preprocessing stage includes data cleaning using the Interquartile Range (IQR) method to remove outliers and noise reduction using the Moving Average method. The results show that the application of data cleaning and noise reduction significantly improves the performance of all classification models. Among the algorithms tested, Random Forest showed the most stable and superior performance in classifying coffee types. These findings confirm that the combination of appropriate data preprocessing and appropriate algorithm selection plays a crucial role in improving the accuracy of machine learning-based coffee classification systems.
Analisis Perbandingan Seleksi Fitur dalam Memprediksi Kelulusan Mahasiswa dengan Menngunakan Artificial Neural Network M. Khoirul Risqi; Ifnu Wisma Dwi Prastya; Mula Agung Barata
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9420

Abstract

Student attrition presents a major challenge in higher education due to its direct impact on academic quality and institutional graduation rates. Detecting students who are likely to withdraw at an early stage is therefore essential to ensure that timely interventions can be made. This study investigates how three distinct feature selection techniques—Chi-Square, Information Gain, and ANOVA—affect the performance of Artificial Neural Networks (ANN) in classifying student outcomes. The data used in the experiment were drawn from academic and administrative records, which had been standardized through Min-Max normalization. The results demonstrate that each method contributes positively, with classification accuracies ranging from 88.71% to 91.37%. Information Gain emerged as the most effective approach, yielding the highest accuracy at 91.37% and a recall score of 97.29%, largely due to its capability to reduce entropy and isolate the most informative variables. ANOVA also performed consistently well with 90.82% accuracy, while Chi-Square was comparatively less effective, potentially due to its reliance on categorical variables that may not capture predictive nuances. These findings emphasize the strategic importance of applying robust feature selection to improve ANN-based prediction models. Ultimately, this research supports the design of data-driven systems aimed at reducing student dropout rates and strengthening academic retention strategies across higher education institutions.
Analisis Sentimen Multi-Platform Media Sosial pada Program Makan Bergizi Gratis Menggunakan Ensemble IndoBERT-SVM Muhammad Bisri Mustofa; Ifnu Wisma Dwi Prastya; Sahri Sahri
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9460

Abstract

Sentiment analysis of public policy on social media faces significant challenges due to linguistic heterogeneity across platforms and limitations of single models in capturing the diversity of opinion expressions. Previous studies tend to employ single-platform and single-model approaches that potentially generate representational bias and accuracy degradation of up to 12–15% when applied to different platform contexts. This study aims to develop a soft voting-based ensemble model that integrates Support Vector Machine (SVM) and IndoBERT to analyze public sentiment toward the Free Nutritious Meal (MBG) Program across multiple platforms, and to evaluate the effectiveness of the ensemble approach compared to single models in addressing variations in linguistic characteristics of digital platforms. The research dataset consists of 7,500 comments from X, TikTok, and YouTube collected from January 6 to September 28, 2025, processed through informal Indonesian language preprocessing, lexicon-based labeling, and stratified split division. Results demonstrate that SVM performs optimally on TikTok (accuracy 98.1%, macro F1 98.0%) but weakly on the neutral class in X (F1 51.0%), while IndoBERT excels in handling pragmatic ambiguity in X (neutral F1 74.0%) despite slightly declining on TikTok (macro F1 93.0%). The ensemble model produces the most balanced performance with accuracies of 92.53% (YouTube), 95.73% (TikTok), 92.53% (X), and macro F1 scores of 85.07%, 94.33%, 84.92% respectively. The contributions of this research include the development of a multi-platform sentiment analysis approach that addresses single-platform bias, improved classification generalization capability across heterogeneous digital ecosystems, and provision of evidence-based evaluation instruments for improving government policy implementation and communication.
Analisis Sentimen Komentar Cyberbullying Terhadap Fenomena Flexing di Tiktok Menggunakan Artificial Neural Network Lailatul Qodriyah; Ifnu Wisma Dwi Prastya; Guruh Purbo Dirgontoro
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9494

Abstract

The rising trend of flexing on TikTok has created a dynamic digital space that often triggers varied user reactions, including subtle forms of cyberbullying. This study aims to analyze public sentiment toward flexing content and evaluate the performance of the Artificial Neural Network (ANN) algorithm in classifying user comments. A total of 4,013 comments were collected through a scraping process on the TikTok account of Miechel Halim and automatically labeled using a lexicon-based approach. The comments were then pre-processed and transformed into Term Frequency–Inverse Document Frequency (TF-IDF) representations before being split into training and testing datasets with an 80:20 ratio. The ANN model was trained under two scenarios before and after the application of the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Experimental results show that the initial model achieved an accuracy of 89.79%, which increased to 90.04% after SMOTE, accompanied by an improvement in the recall of the negative class. These findings indicate that ANN is effective for sentiment classification of TikTok comments, although informal language patterns and highly imbalanced labels remain challenges in identifying negative or potentially harmful remarks related to cyberbullying.
Perbandingan Metode Euclidean dan Manhattan Distance dalam Implementasi Algoritma K-Means dan K-Medoid pada Pengelompokkan Faktor Dominan Perceraian di Kabupaten Bojonegoro Elok Salma Nabila Salma; Ifnu Wisma Dwi Prastya; Ita Aristia Sa’ida
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9520

Abstract

The divorce rate in Bojonegoro Regency continues to increase, driven by various social factors such as constant disputes, economic pressure, and household disharmony. Consequently, an analysis is required to map dominant and non-dominant factors more effectively. This study aims to group the factors causing divorce in Bojonegoro Regency for the 2021–2023 period and determine the most optimal clustering method. The research utilizes K-Means and K-Medoids algorithms with Euclidean and Manhattan distance metrics applied to both raw data and data normalized using the Min–Max Scaler, evaluated via the Silhouette Score. The results indicate that data normalization improves cluster quality, and K-Means with Manhattan distance on normalized data achieves the best performance, yielding a Silhouette Score of 0.849547. Cluster displacement analysis reveals that the grouping patterns remain relatively consistent across years, with "constant disputes" consistently emerging as the dominant factor, while other factors remain in the non-dominant cluster with similar patterns. This study demonstrates that K-Means with Manhattan distance on normalized data is more effective for clustering divorce factors. These findings can serve as a methodological foundation for the local government in formulating data-driven social policies and interventions.
Co-Authors Afril Efan Pajri Ahmad Abil Rizky Ramadhan Basuki Rahmat Devi Nova Kharisma Diah Fitriani Diana Purwitasari Dimas Saputra Dwi Issadari Hastuti Elok Salma Nabila Salma Falach Asy'ari, Misbachul Februyani, Nawafilla Ferdita Inayah Hestu Saputri Guruh Purbo Dirgontoro Hakim, Fernanda Amri Ita Aristia Sa'ida Jauhar Vikri, Muhammad Jauhara Rana Budiani, Jauhara Rana Kholifatus Sholihah Lailatul Qodriyah Lailatul Qodriyah Lambang, Rahmat Tegar Patriot Hari M. Ahsanul Fikri M. Khoirul Risqi M. Khoirul Risqi M. Ridlwan Hambali Meliysa Pasa Bagna Aprilia Said Mohammad Alif Awwalin Muh. Mashdarul Hilmi Aufa Muhammad Bisri Mustofa Muhammad Jauhar Vikri Mula Agung Barata Nuniska Dwi Kamayanti Nur Azizah, Anisa Nur Saifuddin Pradema Sanjaya, Ucta Pribadi , Teguh Rahmat Tegar Patriot Hari Lambang Sahri Sahri Sahri Sahri Sanjaya, Ucta Pradema Shofiatuz Zulfia Shofiatuz Zulfia Siti Azizah Aini suudin aziz Teguh Pribadi Ucta Pradema Sanjaya Yuniar, Intan