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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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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)

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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.
Klasifikasi Dana Hibah Usaha Mikro Kecil dan Menengah dengan Metode Naïve Bayes Sanjaya, Ucta Pradema; Pribadi , Teguh; Prastya, Ifnu Wisma Dwi
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3099

Abstract

Pandemi COVID-19 yang melanda membuat pengusaha mengalami melambatnya perekonomian. Untuk menstimulus perekonomian serta memberikan ketahanan terhadap pengusaha maka pemerintah memberikan dana hibah untuk pengusaha Usaha Micro Kecil dan Menengah. Pemberian dana hibah untuk Usaha Micro Kecil dan Menengah terkadang terdapat masalah dalam pembagiaanya. Dikarenakan terdapat permasalahan tersebut maka perlu adanya model data mining dalam menangani masalah terserbut. Data mining bentuk disiplin ilmu yang memiliki 5 peran antar lain metode klasifikasi. Pada metode klasifikasi yang mengunakan peluang yang ciri perhitungannya adalah metode naïve bayes. Metode naïve bayes sudah banyak digunakan untuk mengklasifikasikan beberapa penelitian terkait dengan ekonomi, kesehatan, dan lain sebagainya. Dari pengunaan naïve bayes, maka akan di evaluasi dengan X-Cross validation/ K-Fold validation. Dari perbandingan pengunaan fold validation maka nilai akurasi terbesar terdapat pada nilai 3 fold validation dengan nilai akurasi sebesar 95,96% dan untuk nilai recall pada percobaan metode fold validation semuanya mendapatkan nilai 100%. Nilai presisi paling tinggi pada percobaan 3 fold validation yaitu sebesar 87,96%.
Easy Data Augmentation untuk Data yang Imbalance pada Konsultasi Kesehatan Daring Nur Azizah, Anisa; Falach Asy'ari, Misbachul; Wisma Dwi Prastya, Ifnu; Purwitasari, Diana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 5: Oktober 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107082

Abstract

Pendekatan augmentasi teks sering digunakan untuk menangani imbalance data pada kasus klasifikasi teks, seperti teks Konsultasi Kesehatan Daring (KKD), yaitu alodokter.com. Teknik oversampling dapat mengatasi kondisi skewed terhadap kelas mayoritas. Namun, augmentasi teks dapat mengubah konten dan konteks teks karena kata-kata teks tambahan yang berlebihan. Penelitian kami menyelidiki algoritma Easy Data Augmentation (EDA), yang berbasis parafrase kalimat dalam teks KKD dengan menggunakan teknik Synonym Replacement (SR), Random Insertion (RI), Random Swap (RS), dan Random Deletion (RD). Kami menggunakan Tesaurus Bahasa Indonesia untuk mengubah sinonim di EDA dan melakukan percobaan pada parameter yang dibutuhkan oleh algoritma untuk mendapatkan hasil augmentasi teks yang optimal. Kemudian, percobaan menyelidiki proses augmentasi kami menggunakan pengklasifikasi Random Forest, Naïve Bayes, dan metode berbasis peningkatan seperti XGBoost dan ADABoost, yang menghasilkan peningkatan akurasi rata-rata sebesar 0,63. Hasil parameter EDA terbaik diperoleh dengan menambahkan nilai 0,1 pada semua teknik EDA mendapatkan 88,86% dan 88,44% untuk akurasi dan nilai F1-score. Kami juga memverifikasi hasil EDA dengan mengukur koherensi teks sebelum dan sesudah augmentasi menggunakan pemodelan topik Latent Dirichlet Allocation (LDA) untuk memastikan konsistensi topik. Proses EDA dengan RI memberikan koherensi yang lebih baik sebesar 0,55 dan dapat mendukung implementasi EDA untuk menangani imbalance data, yang pada akhirnya dapat meningkatkan kinerja klasifikasi.   Abstract   The text augmentation approach is often utilized for handling imbalanced data of classifying text corpus, such as online health consultation (OHC) texts, i.e., alodokter.com. The oversampling technique can overcome the skewed condition towards majority classes. However, text augmentation could change text content and context because of excessive words of additional texts. Our work investigates the Easy Data Augmentation (EDA) algorithm, which is sentence paraphrase-based in the OHC texts that often in non-formal sentences by using techniques of synonym replacement (SR), random insertion (RI), random swap (RS), and random deletion (RD). We employ the Indonesian thesaurus for changing synonyms in the EDA and do empirical experiments on parameters required by the algorithm to obtain optimal results of text augmentation. Then, the experiments investigate our augmentation process using classifiers of Random Forest, Naïve Bayes, and boosting-based methods like XGBoost and ADABoost, which resulted in an average accuracy increase of 0.63. The best EDA parameter results were acquired by adding a value of 0.1 in all EDA techniques to get 88.86% and 88.44% for accuracy and F1-score values. We also verified the EDA results by measuring coherences of texts before and after augmentation using a topic modeling of Latent Dirichlet Allocation (LDA) to ensure topic consistency. The EDA process with RI gave better coherences of 0.55, and it could support the EDA application to handle imbalanced data, eventually improving the classification performance.
Rice Quality Identification Built on Indonesian Food Standards Based on Electronic Nose using Naïve Bayes Algorithm Jauhar Vikri, Muhammad; Wisma Dwi Prastya, Ifnu; Pradema Sanjaya, Ucta; Agung Barata, Mula
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 Teh Bunga Telang sebagai Upaya Peningkatan Keterampilan dan Ekonomi Kreatif Ibu PKK Desa Tapelan Bojonegoro Aziz, Suudin; Prastya, Ifnu Wisma Dwi
Jurnal Pengabdian Masyarakat Vol. 1 No. 3 (2025): Jurnal Pengabdian Masyarakat (J-AbMas)
Publisher : CV. Dalle’ Deceng Abeeayla

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69623/j-abmas.v1i3.192

Abstract

Kegiatan pengabdian kepada masyarakat ini dilaksanakan dalam bentuk pelatihan pembuatan teh bunga telang bagi ibu-ibu PKK Desa Tapelan, Kecamatan Ngraho, Kabupaten Bojonegoro. Tujuan kegiatan adalah meningkatkan pengetahuan dan keterampilan peserta dalam mengolah bunga telang (Clitoria ternatea) menjadi produk minuman herbal yang bernilai jual, sekaligus mendorong pengembangan ekonomi kreatif berbasis potensi lokal. Metode kegiatan meliputi tahap persiapan, penyampaian materi, demonstrasi, praktik langsung, serta evaluasi. Hasil pelatihan menunjukkan bahwa peserta mampu memahami proses pengolahan bunga telang, mulai dari pemilihan bahan, pengeringan, penyeduhan, hingga pengemasan sederhana. Antusiasme dan partisipasi aktif peserta menjadi indikasi keberhasilan kegiatan, sekaligus membuka peluang untuk pengembangan usaha rumah tangga berbasis teh telang. Dengan demikian, pelatihan ini memberikan manfaat nyata bagi peningkatan keterampilan masyarakat serta berpotensi mendukung pemberdayaan ekonomi keluarga di Desa Tapelan.
Indonesian Gold Price Forecasting Using Simple and Stacked LSTM with Expanding Window Lambang, Rahmat Tegar Patriot Hari; Prastya, Ifnu Wisma Dwi; Barata, Mula Agung Barata
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12148

Abstract

This study investigates the performance of two deep learning architectures, namely Simple LSTM and Stacked LSTM, for Indonesian gold price forecasting, with a particular focus on evaluating the effect of optimizer selection and learning rate configurations. An experimental framework is implemented using daily Indonesian gold price data from 2021 to 2024. Model performance is assessed using five-fold expanding window time series cross-validation to ensure robustness and avoid data leakage. Four adaptive training optimizers (Adam, Nadam, Adamax, and RMSprop) are evaluated across three learning-rate settings as part of a systematic sensitivity analysis of training hyperparameters. The results indicate that the Simple LSTM consistently outperforms the Stacked LSTM. The best performance is achieved by the Simple LSTM using the Adam optimizer with a learning rate of 0.01, yielding an RMSE of 9.235, MAE of 7.060, and MAPE of 0.71%. These findings demonstrate that simpler architectures combined with appropriate training configurations can provide superior forecasting accuracy for volatile financial time series.
Sentiment Analysis of the Free Nutritious Meal Program (MBG) on Social Media X (Twitter) Using K-Nearest Neighbor and Artificial Neural Network Hakim, Fernanda Amri; Prastya, Ifnu Wisma Dwi; Budiani, Jauhara Rana
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12205

Abstract

The Free Nutritious Meal Program (Makan Bergizi Gratis/MBG) is a national policy initiated by the Indonesian government to improve public nutritional status, particularly among children and vulnerable groups. Since its implementation, the program has generated extensive public discussion on social media, reflecting diverse opinions, support, and criticism. This study aims to analyze public sentiment toward the MBG program on social media X (Twitter) using machine learning-based text classification methods. A total of 9,038 Indonesian-language tweets were collected and processed through text preprocessing, semi-automatic sentiment labeling with manual validation, and feature extraction using the Term Frequency–Inverse Document Frequency (TF–IDF) method. Sentiments were classified into three categories: positive, neutral, and negative. The performance of K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), and ANN with class balancing using Synthetic Minority Over-Sampling Technique (ANN + SMOTE) was evaluated using accuracy, precision, recall, and F1-score metrics supported by confusion matrix analysis. The results indicate that the ANN + SMOTE model achieved the highest performance with an accuracy of 93.58%, outperforming ANN (92.59%) and KNN (86.28%). The sentiment distribution indicates that public opinion toward the MBG program is predominantly neutral (52.1%), followed by positive (40.0%) and negative (7.9%) sentiments. These findings suggest that while the MBG program is generally well received, negative sentiments provide important feedback related to program implementation and governance.
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.
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