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All Journal TEKNIK INFORMATIKA JURNAL INSTEK (Informatika Sains dan Teknologi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Ilmiah Sinus SENSITEK Generation Journal Respati Journal of Computer System and Informatics (JoSYC) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia International Journal Artificial Intelligent and Informatics Jurnal Permata Indonesia Jurnal Rekayasa elektrika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Ferry Wahyu Wibowo
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology Nursing Public Health

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Calculating vehicle intensiveness increase on eid al-fitr day with anfis method Pratama, Rendy Bagus; Utami, Ema; Wibowo, Ferry Wahyu
International Journal Artificial Intelligent and Informatics Vol 1, No 1 (2018)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (266.592 KB) | DOI: 10.33292/ijarlit.v1i1.10

Abstract

the number of motorized vehicles increases every year, especially private vehicles and is not offset by inadequate access until the road becomes more crowded, even traffic jams occur, especially during public holidays and national holidays. for example, during eid holidays there is a density of traffic flow when going back and forth every year, with the development of current technology the density of traffic flows that occur can be calculated so that it will be easier to anticipate in the future. but in this study only will examine the parameter values that cause the vehicle to occur density and accumulation, because it can be developed with parameter values so that the results can be obtained efficiently in solving traffic density. From the results of the anfis method, efficiency is obtained, namely on h-1 and h days of 2014, and 2017 can use Parameters with magins of 6,3% and 4,32%, while 2015 and 2016 can use parameters with margins 1,79% and 0,79%. and for the h + 1 day of 2014, 2016, and 2017, it is more efficient to use parameters with margins 1,4% and only parameters in 2015 which have the efficiency value using parameters with margin -6,17. anfis application in this calculation can be developed in a prediction system.
Expert system for diagnosing diphtheria with k-nearest neighbor method Fatoni, Chavid Syukri; Utami, Ema; Wibowo, Ferry Wahyu
International Journal Artificial Intelligent and Informatics Vol 1, No 2 (2018)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (430.82 KB) | DOI: 10.33292/ijarlit.v1i1.4

Abstract

The Diphtheria cases have special concern by the Indonesian government and are recorded as an extraordinary case (KLB) in 2017. Diphtheria is an infectious disease and cause complications of dangerous and deadly diseases if have not any treated immediately. Along this time, the communities often underestimate the common symptoms of diseases, such as throat pain, flu, and fever. The similarity of Diphtheria symptoms with common diseases and complications such as myocarditis, obstruction on breath, Acute Kidney Injury (AKI), making Diphtheria are rather difficult to treat due to the infections spread quickly. Some complications of diphtheria can cause a death if have not treated immediately and there must be any identification early for diphtheria. Then, an expert system is needed to help the community and the government in diagnosing the diphtheria. An expert system is an information system containing knowledge from experts in order provide information to be used for consultation. The knowledge from experts in this particular system is used as a basis by the Expert System to answer the questions (consultation). The study used the K-Nearest Neighbor (KNN) method, which the method calculates the similarity value of Diphtheria disease symptom. As the result, it can provide an initial diagnosis for Diphtheria before complications occur. The output of this study is the diagnosis of diphtheria based on the symptoms with the accuracy results of 93.056%, as well as providing an initial diagnosis in order to have immediately treating the diphtheria. 
Klasifikasi Penyakit Pada Daun Jagung Menggunakan Convolutional Neural Network Akhyari, Muhammad Wafa; Suyoto, Andi; Wibowo, Ferry Wahyu
Jurnal Informa : Jurnal Penelitian dan Pengabdian Masyarakat Vol 7 No 2 (2021): Desember
Publisher : Politeknik Indonusa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46808/informa.v7i2.199

Abstract

Deep Learning masih diteliti secara luas dan masih menjadi masalah yang menarik. Pada penelitian ini daun pada jagung di gunakan sebagai objek penelitian sedangkan Deep Learning digunakan untuk memproses dan mendiagnosa penyakit tanaman pada daun jagung menggunakan metode Convolutional Neural Network (CNN), sebanyak 3.846 gambar pada daun tanaman jagung, yang terdiri dari tiga jenis penyakit jagung yaitu penyakit Bercak Daun, Hawar Daun dan Karat Daun digunakan sebagai dataset. Dengan hasil akurasi keseluruhan di atas 90%, dalam mendeteksi penyakit pada tanaman jagung berdasarkan daunnya.
Leveraging Vector Quantized Variational Autoencoder for Accurate Synthetic Data Generation in Multivariate Time Series Diqi, Mohammad; Utami, Ema; Kusrini, Kusrini; Wibowo, Ferry Wahyu
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4514

Abstract

This study addresses the challenge of generating high-quality synthetic financial time series data, acritical issue in financial forecasting due to limited access to complete and reliable historical datasets.The aim of this research was to compare the performance of the standard Variational Autoencoder andthe Vector Quantized Variational Autoencoder (VQ-VAE) in generating synthetic multivariate time seriesdata using the Adaro Energy Indonesia stock dataset. The VQ-VAE incorporates a discrete latentspace to improve the structure and control of the data generation process, whereas the standard VAEutilizes a continuous latent space. This research method was based on the implementation of bothmodels, followed by a quantitative evaluation using statistical metrics, including mean absolute error(MAE), mean squared error (MSE), root mean squared error (RMSE), and R² score. This researchshowed that the VQ-VAE outperformed the standard VAE in replicating the statistical characteristicsof stock prices, as shown by lower error values and higher R² scores across all tested features. The discretelatent space of the VQ-VAE led to the generation of more structured and statistically consistentsynthetic data. The implications of these findings suggest that the VQ-VAE model is highly suitablefor financial forecasting applications and indicate the potential for future enhancements throughintegration with hybrid models, such as attention mechanisms or generative adversarial networks.
VARFIS: A Hybrid Neuro-Fuzzy Model for Intelligent Microclimate Control in Black Soldier Fly Farming Systems Yunita Sartika Sari; Kusrini; Ema Utami; Ferry Wahyu Wibowo
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.46610

Abstract

Maintaining optimal microclimate conditions is essential for Black Soldier Fly (BSF) cultivation, yet traditional systems often struggle with dynamic environmental changes. This study proposes the Vector Autoregressive-Fuzzy Inference System (VARFIS), a hybrid model combining Vector Autoregression (VAR) and Adaptive Neuro-Fuzzy Inference System (ANFIS), to enhance temperature and humidity control in BSF insectariums. VARFIS adapts to uncertainty using probabilistic learning, achieving a 48% reduction in prediction error (MAPE = 1.36%) and high accuracy (R² = 0.9695), outperforming standalone VAR and ANFIS models. The model effectively captures daily climate fluctuations, improving larval growth efficiency and waste conversion. However, it remains limited in handling extreme events such as sudden heatwaves or humidity spikes, indicating the need for enhancements like adaptive fuzzy rule tuning and integration of physical constraints. VARFIS presents a scalable solution for intelligent microclimate management, supporting sustainable insect farming and circular economy goals. This work contributes to precision agriculture by offering data-driven tools for resilient environmental control.
Improving Infant Cry Recognition Using MFCC And CNN-Based Audio Augmentation Setyoningrum, Nuk Ghurroh; Utami, Ema; Kusrini, Kusrini; Wibowo, Ferry Wahyu
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4373

Abstract

Recognizing infant cries is essential for understanding a baby's needs; however, previous research has struggled with imbalanced datasets and limited feature extraction techniques. Conventional methods utilizing CNN without data augmentation often failed to accurately classify minority classes such as belly pain, burping, and discomfort, resulting in biased models that predominantly recognized majority classes. This study proposes an MFCC-based data augmentation pipeline, incorporating time stretching, pitch scaling, noise addition, polarity inversion, and random gain adjustments to increase dataset diversity and enhance model generalization. By applying this approach, the dataset size was expanded from 457 to 8,683 samples, and a CNN model with three convolutional layers, ReLU activation, and max pooling was trained for cry pattern classification. The results indicate a substantial accuracy improvement from 78% to 98%, with F1-scores for minority classes rising from 0.00 to above 0.90, confirming that augmentation effectively addresses dataset imbalance. This research advances computer science and artificial intelligence, particularly in audio signal processing and deep learning for healthcare applications, by demonstrating the role of data augmentation in improving cry classification performance. Future directions include integrating multimodal data (visual and physiological signals), exploring advanced deep learning architectures, and developing real-time applications for smart baby monitoring systems to further enhance infant cry recognition technology.
Yayak Kartika Sari Prediksi Customer Churn Berbasis Adaptive Neuro Fuzzy Inference System Sari, Yayak Kartika; Kusrini, Kusrini; Wibowo, Ferry Wahyu
Generation Journal Vol 2 No 1 (2018): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (905.836 KB) | DOI: 10.29407/gj.v2i1.12054

Abstract

Abstrak – Customer Churn adalah pelanggan yang berhenti berlangganan dan pindahpada perusahaan lain, karena berbagai faktor. Customer churn merupakan masalah yang sangatpenting yang harus dihadaapi oleh perusahaan karena berhentinya pelanggan akan berdampakpada retensi perusahaan. Oleh sebab itu, dibuatkan sistem prediksi customer churn untukmengetahui tingkat pelanggan yang churn, apabila customer churn dapat diketahui terlebih dahulu,maka akan menguntungkan bagi pihak CRM untuk mengatur strategi-strategi mencegah pelangganyang melakukan churn. Untuk menentukan prediksi customer churn menggunakan teknik datamining dengan algoritma ANFIS. Algoritma ANFIS merupakan gabungan antara jaringan syaraftiruan dengan fuzzy inference system. Model prediksi yang dibangun dengan metode ANFISmenggunakan pembelajaran alur maju dan pembelajaran alur mundur, sehingga untuk melakukanprediksi dibutuhkan nilai parameter fuzzy baru yang diperoleh dari proses pelatihan. Setelah nilaiparameter fuzzy baru didapatkan, maka akan dilakukan tahap pengujian. Pada tahap pengujiandilakukan dengan proses pembelajaran maju untuk mendapatkan nilai prediksinya, sehingga padaprosesnya nilai prediksi yang berupa angka dan status prediksi. Pelatihan dan pengujian ANFISuntuk semua produk menghasilkan perbandingan nilai error rata-rata pelatihan sebesar 8,316 %
Co-Authors Akhyari, Muhammad Wafa Aldy A Kulakat Ali Asgar Zainal Abidin Andi Sunyoto Arief Setyanto Arif Fajar Solikin Azis Wahyudi Bayu Setiaji Chavid Syukri Fatoni Darno - Ema Utami Fatoni, Chavid Syukri KUSRINI Kusrini - Kusrini Kusrini Kusrini Kusrini Kusrini, Kusrini Mohammad Diqi Nuk Ghurroh Setyoningrum Prastowo, Wahit Desta Pratama, Rendy Bagus Rachmawati Oktaria Mardiyanto Sari, Yayak Kartika Sari, Yunita Sartika Solikin, Arif Fajar Suyoto, Andi Widhiarta, Widhiarta Wihayati, Wihayati