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All Journal JPM (Jurnal Pemberdayaan Masyarakat) Jurnal SOLMA JURIKOM (Jurnal Riset Komputer) Journal of Information Systems and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Jifosi
Ifnu Wisma Dwi Prastya
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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IMPLEMENTASI ALGORITMA C4.5 DALAM DIAGNOSIS AUTISME PADA ANAK MENGGUNAKAN RUMUSAN DIAGNOSTIC AND STATISTICAL MANUAL OF MENTAL DISORDERS V Ifnu Wisma Dwi Prastya; Yuniar, Intan; Rahmat, Basuki
Jurnal Informatika dan Sistem Informasi (JIFoSI) Vol. 1 No. 2 (2020): JIFoSI Volume 1, No 2: Juli 2020
Publisher : Fakultas Ilmu Komputer Universitas Pembangunan Nasional Veteran Jawa Timur

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Abstract

Abstrak        Diagnosis autisme merupakan langkah pertama dalam proses penanganan autisme. Namun, permasalahannya banyak orang tua yang masih belum mengerti terkait gejala yang dialami oleh anaknya dan bagaimana cara penagannya. Masih banyak orang tua yang memilih untuk langsung berkonsultasi kedokter atupun tenaga medis. Sedangkan jumlah dokter atau tenaga medis dalam bidang gangguan perkembanagan mental dan otak masih sangant sedikit. Maka dari itu, dibutuhkan cara pendiagnosisan autisme secara mudah dan gampang diakses oleh orang tua, sehingga orang tua dapat dengan mudah mendiagnosis secara dini autisme pada anak. Algoritma C4.5 merupakan salah satu algoritma yang dapat memprediksi tingkat akurasi diagnosis autisme dan Diagnostic and Statistical Manual of Mental Disorders merupakan sebuah acuan yang digunakan untuk mendiagnosa suatu gangguan kejiwaan.          Penelitian ini menggunakan 70 data, dengan pembagian data dengan komposisi 70% untuk data latih dan 30 % data uji, sehingga ditemukan 50 data untuk digunakan sebagai data latih dan 20 data untuk data uji. Pengujian dalam sistem ini menggunakan metode Confusion Matrix. Pohon keputusan yang terbangun dari sistem ini memiliki nilai akurasi sebesar 90%, dan menghasilkan nilai precision sebesar 93,33% dan nilai recall sebesar 93,33%.   Kata Kunci : Diagnosis, Autisme, Algoritma C4.5, DSM-V  (Diagnostic and Statistical Manual of Mental Disorders V ). The diagnosis of autism is the first step in the process of treating autism. However, the problem is that many parents still do not understand the symptoms associated with their children and how to treat them. There are still many parents who choose to consult a doctor or a medical person directly. While the number of doctors or medical personnel in the field of mental and brain development disorders is still small. Therefore, it is needed a way to diagnose autism easily and easily accessed by parents, so parents can easily diagnose early autism in children. C4.5 algorithm is one algorithm that can predict the accuracy of the diagnosis of autism and the Diagnostic and Statistical Manual of Mental Disorders is a reference used to diagnose a psychiatric disorder. This study uses 70 data, with the division of data with a composition of 70% for training data and 30% for test data, so that 50 data are found to be used as training data and 20 data for test data. Testing in this system uses the Confusion Matrix method. The decision tree that was built from this system has an accuracy value of 90%, and produces a precision value of 93.33% and a recall value of 93.33%. Keywords: Diagnosis, Autism, C4.5 Algorithm, DSM-V (Diagnostic and Statistical Manual of Mental Disorders V).
Pelatihan Dan Penyuluhan Pembuatan Lilin Aromaterapi Dari Limbah Tembakau Di Desa Gunungrejo Kedungpring Lamongan Februyani, Nawafilla; Ifnu, Ifnu Wisma Dwi Prastya
Jurnal SOLMA Vol. 14 No. 1 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

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Abstract

Background: Tobacco is an important agricultural commodity with various benefits, including for natural pesticides, cosmetics, and essential oils. One potential innovation from tobacco waste is the manufacture of aromatherapy candles, which have health benefits such as reducing stress and improving quality of life. This community service program aims to utilize tobacco waste through training and counseling on making aromatherapy candles for 25 PKK members of Gunungrejo Village, Kedungpring District, Lamongan Regency. Methods: The methods used include counseling, socialization of the benefits of aromatherapy candles, and candle-making practices. Results: The results of the program showed an increase in participant understanding from 47.9% to 98.2% based on the pretest and posttest. Conclusions: In addition to social benefits in the form of health awareness and community strengthening, this program also opens up economic opportunities with the potential for increased income through the development of aromatherapy candle businesses based on tobacco waste.
Perbandingan Algoritma Machine Learning untuk Klasifikasi Kopi Menggunakan Data Sensor Electronic Nose dan Tongue Dwi Issadari Hastuti; Mula Agung Barata; Ifnu Wisma Dwi Prastya
JURNAL RISET KOMPUTER (JURIKOM) 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 Sentimen Multi-Platform Media Sosial pada Program Makan Bergizi Gratis Menggunakan Ensemble IndoBERT-SVM Muhammad Bisri Mustofa; Ifnu Wisma Dwi Prastya; Sahri, Sahri
JURNAL RISET KOMPUTER (JURIKOM) 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
JURNAL RISET KOMPUTER (JURIKOM) 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 Salma, Elok Salma Nabila; Ifnu Wisma Dwi Prastya; Ita Aristia Sa’ida
JURNAL RISET KOMPUTER (JURIKOM) 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.
Evaluasi Komparatif Metode Feature Selection pada XGBoost Regression untuk Prediksi Panjang Siklus Menstruasi Shofiatuz Zulfia; Mula Agung Barata; Ifnu Wisma Dwi Prastya
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 3 (2026): Maret 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i3.9526

Abstract

Panjang siklus menstruasi menjadi indikator utama dalam kesehatan reproduksi perempuan, namun perbedaan karakteristik individu dan ketidakteraturan siklus menyulitkan proses prediksi secara manual. Kondisi tersebut mendorong perlunya pendekatan berbasis data yang mampu menghasilkan prediksi panjang siklus menstruasi secara akurat dan konsisten. Penelitian ini bertujuan untuk melakukan evaluasi komparatif berbagai metode feature selection pada algoritma XGBoost Regression dalam memprediksi panjang siklus menstruasi. Dataset penelitian diperoleh dari Kaggle dan terdiri atas 162 data yang mencakup atribut fisiologis dan demografis perempuan. Tahapan penelitian meliputi preprocessing data, normalisasi menggunakan StandardScaler, pembagian data latih dan data uji dengan rasio 80:20, serta validasi 10-fold cross-validation untuk menguji stabilitas model. Empat skenario pemodelan dievaluasi, yaitu tanpa feature selection sebagai baseline, forward selection, backward elimination, dan optimized selection berbasis ensemble feature selection dari lima metode seleksi fitur. Hasil evaluasi menunjukkan bahwa metode forward selection memberikan performa terbaik dengan nilai R² sebesar 0,9005, RMSE 1,45 hari, MAE 0,57 hari, dan MAPE 1,73% (kesalahan relatif rata-rata < 2% terhadap panjang siklus 25-30 hari), serta meningkatkan nilai R² sebesar 0,1696 poin (dari 0,7309 menjadi 0,9005), setara dengan peningkatan relatif 23,2% terhadap nilai baseline. Temuan ini menunjukkan bahwa pemilihan metode feature selection yang tepat berpengaruh terhadap peningkatan performa prediktif dan stabilitas model XGBoost Regression dalam prediksi panjang siklus menstruasi.
Optimization of Sleep Disorder Classification Using ANN with Multi-Method Feature Selection Devi Nova Kharisma; Ifnu Wisma Dwi Prastya; Ita Aristia Saida
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1473

Abstract

Sleep disorders are health problems that can affect quality of life and have the potential to increase the risk of various chronic diseases. Therefore, a computational approach is needed to accurately and efficiently classify sleep disorders. The ANN model used has a two-layer hidden architecture with 128 and 64 neurons, respectively, and uses the ReLU activation function, equipped with a dropout layer to reduce overfitting. Three neurons with a softmax activation function make up the output layer, which produces probabilities for every class. To improve model performance, three feature selection methods were compared, namely Chi-Square, Information Gain, and Pearson Correlation. The test results showed that the ANN model without feature selection produced an accuracy of 89.3%. After feature selection, the model's performance improved significantly. The Chi-Square method produced 8 selected features with the highest accuracy of 97.3%, followed by Information Gain with 5 features and an accuracy of 97.3%, and Pearson Correlation with 3 features and an accuracy of 88.0%. The results of this study demonstrate that selecting appropriate features can significantly enhance an ANN's ability to categorize sleep problems. The proposed approach is expected to be a reference in the development of a more accurate sleep disorder diagnostic aid system.
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.
Co-Authors Basuki Rahmat Devi Nova Kharisma Dwi Issadari Hastuti Februyani, Nawafilla Guruh Purbo Dirgontoro Ita Aristia Sa'ida Ita Aristia Saida Lailatul Qodriyah M. Ridlwan Hambali Muhammad Bisri Mustofa Mula Agung Barata Sahri Sahri Salma, Elok Salma Nabila Shofiatuz Zulfia Shofiatuz Zulfia Teguh Pribadi Ucta Pradema Sanjaya Yuniar, Intan