Article Per Year (5 Year)
p-Index From 2021 - 2026
1.564
P-Index
This Author published in this journals
All Journal JPM (Jurnal Pemberdayaan Masyarakat) Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Jifosi Jurnal Informatika: Jurnal Pengembangan IT INOVTEK Polbeng - Seri Informatika NAWASENA BHAKTI
Ifnu Wisma Dwi Prastya
Unknown Affiliation
Religion Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Public Health Social Sciences Other

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 10 Documents
Search

 1 
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (528.425 KB)

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)

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

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.
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)

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

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 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

Penilaian kualitas tembakau masih banyak dilakukan secara manual oleh tenaga ahli, sehingga rentan terhadap subjektivitas dan inkonsistensi antarpenilai. Masalah ini dapat menghambat proses kontrol mutu, terutama ketika volume produksi tinggi dan diperlukan keputusan cepat serta akurat. Penelitian ini bertujuan untuk mengembangkan pendekatan klasifikasi kualitas tembakau berbasis pembelajaran mesin yang terintegrasi dengan perangkat electronic nose (E-Nose) sebagai solusi yang lebih objektif dan terukur. Sebanyak 375 sampel tembakau yang mewakili empat tingkat kekeringan dikumpulkan dan diolah menggunakan metode Interquartile Range (IQR) untuk menghilangkan outlier serta Moving Average untuk mereduksi noise pada sinyal sensor MQ-4, MQ-7, dan MQ-135. Dua algoritma klasifikasi, yaitu C4.5 dan Random Forest, diterapkan dan dievaluasi menggunakan stratified 10-fold cross-validation untuk memperoleh estimasi performa yang stabil. Hasil penelitian menunjukkan bahwa Random Forest memberikan performa terbaik dengan akurasi 93%, nilai Cohen’s Kappa 0.9259, MCC 0.9262, balanced accuracy 0.945, dan cross-entropy log loss 0.4291. Temuan ini memperlihatkan bahwa integrasi E-Nose dengan teknik ensemble learning mampu meningkatkan keandalan proses identifikasi kualitas tembakau. Secara keseluruhan, penelitian ini menyimpulkan bahwa sistem klasifikasi berbasis E-Nose dan pembelajaran mesin dapat dijadikan dasar pengembangan teknologi otomasi penilaian tembakau yang lebih cepat, konsisten, dan siap diterapkan pada skala industri
Rice Quality Identification Built on Indonesian Food Standards Based on Electronic Nose using Naïve Bayes Algorithm Muhammad Jauhar Vikri; Ifnu Wisma Dwi Prastya; Ucta Pradema Sanjaya; Mula Agung Barata
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: 10.35314/0y0xct32

Abstract

Rice is a staple food in Indonesia, where its quality is regulated by the National Food Standards outlined in National Food Agency Regulation No. 2 of 2023 on Rice Quality and Labeling Requirements. Rice is classified into four grades: premium, medium 1, medium 2, and medium 3. The widespread practice of mislabeling lower-quality rice as a premium through repackaging highlights the critical need for quality control measures. An electronic nose (e-nose) is a reliable device for food quality control. Previous studies have demonstrated its ability to classify rice into two quality grades with 80% accuracy. This study uses exponential data transformation and the Naive Bayes algorithm to enhance the classification accuracy for four rice quality grades according to national standards. The methodology includes signal acquisition, feature extraction using statistical parameters, exponential data transformation, classification, and performance evaluation. The results show that exponential data transformation improves classification accuracy to 97%. This technology can be implemented for automated quality control in milling facilities, storage warehouses, and distribution centres, ensuring consistent rice quality while enhancing supply chain efficiency. The e-nose-based model offers a fast and reliable solution, minimising reliance on human operators.
Pelatihan Pembuatan Lilin Abadi sebagai Upaya Peningkatan Keterampilan dan Kemandirian Ekonomi Masyarakat Ifnu Wisma Dwi Prastya
Nawasena Bhakti Vol. 2 No. 1 (2026): Nawasena Bhakti: Jurnal Pengabdian Masyarakat
Publisher : Badan Usaha Milik Desa Berkaho Pungpungan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64084/nawasenabhakti.v2i1.179

Abstract

The economic problems of rural communities and the low utilization of household waste into economically valuable products remain challenges in efforts to improve community welfare. Used cooking oil waste, which is generally discarded directly, has the potential to cause environmental pollution if not managed properly. This community service activity aims to enhance the skills and economic independence of the community through training in making eternal candles in Gunungrejo Village, Kedungpring District, Lamongan Regency. The method of implementing the activities was carried out through several stages, namely preparation, socialization, practical training, evaluation, and follow-up community assistance. The training was conducted on December 24, 2024, using a hands-on method for processing used cooking oil into eternal candles that have utility and market value. The results of the activities showed an increase in the knowledge and skills of the community in utilizing household waste into creative products, as well as the emergence of motivation to develop home-based businesses centered on eternal candles. This activity has a positive impact on increasing environmental awareness, productive skills, and opportunities for sustainable economic independence of the community.
Attention-Enhanced Multivariate Forecasting for Intelligent Microservice Autoscaling Nur Saifuddin; Mula Agung Barata; Ifnu Wisma Dwi Prastya
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12662

Abstract

Proactive autoscaling in cloud-native microservices requires anticipatory decisions because reactive controllers often lag under abrupt workload shifts. This study aims to improve autoscaling decision quality through a two-stage machine learning pipeline. The research adopts an experimental design using production-grade microservice traces, with strict time-respecting train-validation-test splits and training-only fitting for preprocessing and oracle-threshold estimation to prevent leakage. In the first stage, multivariate forecasting models predict future CPU and memory utilization from engineered temporal features. In the second stage, the predicted signals are combined with observed features to classify three autoscaling actions: scale down, hold, and scale up. Benchmarking shows recurrent neural models are strong baselines, while an attention-enhanced encoder-decoder performs best. The best Bahdanau-attention model with residual connection reduces test CPU RMSE from 0.030977 to 0.028924 and memory RMSE from 0.010322 to 0.005452 relative to the strongest BiLSTM baseline. For decision learning, the optimized Extreme Gradient Boosting model using prediction-augmented features achieves an accuracy of 0.950602 and an F1 score of 0.951026. Supporting downstream validation also yields lower SLO violation rates than horizontal and vertical baselines while maintaining zero downtime in the evaluated scenarios. These findings indicate that improving forecasting quality and explicitly transferring predictive signals to the decision stage strengthens proactive autoscaling performance.
TikTok Sentiment Analysis on Koperasi Merah Putih Using SVM and ANN Meliysa Pasa Bagna Aprilia Said; Kholifatus Sholihah; Ifnu Wisma Dwi Prastya; Afril Efan Pajri
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13394

Abstract

TikTok has become a relevant social media source for observing public responses to public issues, including the Koperasi Merah Putih program. This study compares Support Vector Machine (SVM) and Artificial Neural Network (ANN) for classifying sentiment in TikTok comments. The dataset was obtained through TikTok comment scraping and consisted of 25,669 raw comments. After removing empty comments and applying preprocessing stages consisting of cleaning, case folding, normalization, tokenization, stopword removal, and stemming, 21,026 comments were used for sentiment analysis. Sentiment labels were generated automatically using a lexicon-based sentiment labeling approach and grouped into three classes: positive, negative, and neutral. TF-IDF was used for feature extraction with a maximum of 5,000 features and unigram-bigram representation. The dataset was split into training and testing sets with an 80:20 ratio, while Stratified K-Fold Cross Validation and SMOTE were applied to strengthen evaluation and address class imbalance. The results show that SVM achieved the best overall performance before SMOTE with an accuracy of 86.66% and an F1-score of 86.76%. ANN achieved an accuracy of 85.31% before SMOTE and improved slightly after SMOTE to 85.47%. These findings indicate that SVM is more stable for TF-IDF-based TikTok comment classification, while SMOTE can improve ANN performance slightly but does not always increase all models equally.
Co-Authors Afril Efan Pajri Basuki Rahmat Devi Nova Kharisma Dimas Saputra Februyani, Nawafilla Ita Aristia Saida Jauhar Vikri, Muhammad Kholifatus Sholihah M. Ridlwan Hambali Meliysa Pasa Bagna Aprilia Said Muh. Mashdarul Hilmi Aufa Mula Agung Barata Nur Saifuddin Rahmat Tegar Patriot Lambang Shofiatuz Zulfia Shofiatuz Zulfia Teguh Pribadi Ucta Pradema Sanjaya Yuniar, Intan