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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Sarjana Teknik Informatika Jurnal Simantec Jurnal Informatika dan Teknik Elektro Terapan International conference on Information Technology and Business (ICITB) JURNAL INSTEK (Informatika Sains dan Teknologi) Jiko (Jurnal Informatika dan komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terpadu JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) International Journal of Advances in Data and Information Systems Jifosi Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Diferensial Jurnal Informatika Teknologi dan Sains (Jinteks) Makara Journal of Technology Malcom: Indonesian Journal of Machine Learning and Computer Science J-Icon : Jurnal Komputer dan Informatika Jurnal Sistem Informasi dan Informatika Indonesian Journal on Data Science Emerging Statistics and Data Science Journal Signal and Image Processing Letters
Wahyu Syaifullah Jauharis Saputra
Universitas Pembangunan Nasional Veteran Jawa Timur
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health

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Prediksi Suhu Udara Kota Surabaya Menggunakan Prophet dengan Grid Search Hyperparameter Kemal Fahreza Jibran Jibran; Rizky Parlika; Wahyu Syaifullah Jauharis Saputra
Jurnal Sarjana Teknik Informatika Vol. 14 No. 2 (2026): Juni
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v14i2.32011

Abstract

Perubahan iklim meningkatkan jumlah panas yang mencapai permukaan terutama wilayah inti perkotaan seperti Surabaya yang terkena dampak urbanisasi dan fenomena Urban Heat Island. Model ini memprediksi suhu dengan cara yang seakurat mungkin, dan kondisi ini menuntut model prediksi suhu udara harian. Penelitian ini bertujuan untuk memprediksi suhu udara harian Kota Surabaya menggunakan model deret waktu Prophet yang dioptimasikan menggunakan Grid Search Hyperparameter. Sample data sebanyak 2.182 observasi setiap triwulan mulai Januari 2020 hingga akhir Desember 2025. Tahapan penenitian meliputi pengumpulan data, prapemrosesan, transformasi, pembagian data secara-kronologis, pelatihan model baseline, optimasi hyperparameter, and evaluasi kinerja. RMSE 0,868362, MAE 0,660211, dan MAPE 2,325906 adalah model prophet baseline RMSE, MAE, and MAPE. Setelah dilakukan optimasi pada parameter changepoint_prior_scale, seasonality_prior_scale, and seasonality_mode, diperoleh peningkatan kinerja dengan nilai evaluasi RMSE 0,858426, MAE 0,657965, and MAPE 2,311441. Hasil dekomposisi menunjukkan adanya tren jangka panjang serta pola musiman tahunan yang dominan. Optimasi hyperparameter terbukti secara keseluruhan meningkatkan prediksi suhu udara di Surabaya.
PREDIKSI MENGUNAKAN SUPPORT VECTOR REGRESSION DENGAN OPTIMASI GENETIC ALGORITHM: Naomi Dwi Anggraini; Wahyu Syaifullah Jauharis Saputra; Amri Muhaimin
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.v10i1.16884

Abstract

Cabai merupakan komoditas yang memiliki pengaruh signifikan terhadap stabilitas harga pangan di Indonesia, terutama di Provinsi Jawa Timur sebagai sentra produksi dan wilayah dengan tingkat konsumsi cabai yang tinggi. Harga cabai yang berfluktuatif akibat faktor musim, distribusi pasokan, dan permintaan pasar menimbulkan ketidakpastian yang berdampak pada inflasi, daya beli masyarakat, serta strategi pemerintah dalam pengendalian harga pangan. Oleh karena itu, diperlukan model prediksi yang akurat dan mampu menangkap karakteristik data harga cabai yang bersifat non-linear. Penelitian ini bertujuan membangun model prediksi harga komoditas cabai di Jawa Timur menggunakan metode Support Vector Regression (SVR) yang dioptimasi dengan Genetic Algorithm (GA). Proses optimasi dilakukan untuk memperoleh parameter terbaik, dengan pemilihan lag berdasarkan analisis Partial Autocorrelation Function (PACF) dan penggunaan kernel Radial Basis Function (RBF). Hasil pengujian menunjukkan bahwa SVR-GA memberikan peningkatan akurasi yang signifikan dibanding dengan SVR tanpa optimasi, dengan nilai MAPE sebesar 1,96% untuk cabai rawit, 2,34% untuk cabai merah besar, dan 2,38% untuk cabai merah keriting. Temuan ini menegaskan bahwa integrasi SVR dan GA mampu menghasilkan model prediksi yang lebih akurat dan stabil, sehingga dapat dimanfaatkan sebagai alat bantu dalam memantau tren harga dan mendukung pengambilan keputusan terkait pengelolaan komoditas cabai
Optimization of Palm Fruit Ripeness Detection With Yolov11 on CPU Iqbal Ramadhan Anniswa; Wahyu Syaifullah JAUHARIS SAPUTRA; Mohammad Idhom; Alfan Rizaldy Pratama; I Gede Susrama Mas Diyasa
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111253

Abstract

The palm oil industry is one of the strategic sectors that contributes significantly to the Indonesian economy. However, this industry still faces various challenges, particularly in terms of operational efficiency and the implementation of digitalization, especially at the level of independent farmers who often still use manual methods to determine the ripeness of the fruit. This manual process is prone to subjectivity, which can impact harvest quality and supply chain efficiency. To address this issue, this study proposes a palm oil fruit ripeness detection system based on the YOLOv11 algorithm, chosen for its advantages in inference speed and detection accuracy, especially when run on devices with limited resources. The developed model was then implemented using the ONNX Runtime Framework. This enables accelerated inference processes and supports portability on hardware with limited resources. Test results show that the model achieves an mAP@50 accuracy of 90.2% with an average latency of around 255 ms to 300 ms. With these achievements, this system is not only reliable in detecting fruit ripeness, but also efficient in processing time and relevant to support digital transformation in the palm oil plantation sector.
Klasifikasi Gerakan Bahasa Isyarat Indonesia (Bisindo) menggunakan Arsitektur Transfer Learning Xception Meisya Vira Amelia; Wahyu Syaifullah Jauharis Saputra; Kartika Maulida Hindrayani
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.15674

Abstract

Human communication generally relied on speech. However, this was not applicable to the deaf people, who depended on sign language for daily interactions. Unfortunately, not everyone had the ability to understand sign language. In higher education environments, the lack of individuals proficient in sign language often created inequality in the learning process for deaf students. This limitation could be addressed by fostering a more inclusive environment, one of which was through the implementation of a sign language translation system. Therefore, this study aimed to develop a machine learning model capable of detecting and translating Indonesian Sign Language (BISINDO) alphabet gestures. The model was built using the Xception transfer learning method from Convolutional Neural Networks (CNN). The dataset consisted of 26 BISINDO alphabet gestures with a total of 650 images. The model was evaluated using K-Fold cross-validation and achieved an F1-score of 98% during testing
Rice Price Forecasting Using an Ensemble GRU–SVR Model with Enhanced Feature Engineering Dandi Nur Faizi; Trimono Trimono; Wahyu Syaifullah Jauharis Saputra
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3532

Abstract

Rice price volatility significantly impacts economic stability and food security in Indonesia, particularly in East Java, where fluctuations in staple food prices affect household purchasing power and inflation management. This study addresses the limitations of existing rice price forecasting models, which often struggle to capture the complex, nonlinear dynamics of agricultural prices influenced by multiple factors such as climate variability and market conditions. Accurate and reliable price forecasting is essential to support effective policy formulation, market intervention, and food price stabilization strategies. This research develops an ensemble forecasting framework integrating Gated Recurrent Unit (GRU) and Support Vector Regression (SVR) with enhanced feature engineering to predict daily medium rice prices using historical price and weather data. The dataset comprises daily observations from 2021 to 2025, including rice prices, average temperature, relative humidity, rainfall, and sunshine duration. In this framework, GRU serves as a temporal feature extractor to learn complex temporal dependencies, while enhanced feature engineering generates complementary statistical features from sliding windows to enrich GRU's output. The combined feature set is provided to an SVR model with a Radial Basis Function kernel for final regression. Experimental results show that the proposed model achieves a high forecasting accuracy with an MAPE of 0.109%, demonstrating stable predictive behavior and making it a valuable tool for monitoring rice prices. The model's effectiveness in capturing temporal dependencies and nonlinear patterns suggests potential applicability beyond East Java, offering broader insights for agricultural price forecasting in other regions.
Generalized Autoregressive Conditional Heteroskedasticity Approach for Television Program Viewership Trend Analysis Alyssa Amorita Azzah; Aviolla Terza Damaliana; Wahyu Syaifullah Jauharis Saputra
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3710

Abstract

This study aims to determine whether daily television audience dynamics exhibit statistically significant conditional variance dependence that is systematically overlooked in conventional ARIMA-based broadcasting forecasts and to assess the incremental empirical value of integrating ARIMA with GARCH modeling. Using 1,096 consecutive daily observations (2022–2024) of viewers for a nationally broadcast program, we implement a diagnostic-first framework that jointly evaluates conditional mean and variance processes. Stationarity is confirmed through the Augmented Dickey–Fuller test (ADF = −3.4693, p = 0.0088), and an MA(1) specification is selected for the conditional mean (AIC = 1302.76). Residual diagnostics reveal pronounced ARCH effects (ARCH-LM = 78.4602, p < 0.001), justifying second-moment modeling. Among competing variance specifications, GARCH(2,2) yields the lowest information criterion (AIC = 1060.321) and indicates near-unit volatility persistence (Σα + Σβ = 0.9856), evidencing durable intertemporal uncertainty transmission. Out-of-sample forecast evaluation demonstrates low relative error (MAPE ≈ 1.0%), supporting empirical robustness. Unlike prior ARIMA-centered broadcasting studies that prioritize point accuracy under homoscedastic assumptions, this integration explicitly models volatility clustering as an object of inference, aligning media analytics with established volatility frameworks without overstating cross-domain novelty. The findings show that incorporating conditional variance dynamics provides measurable gains in risk-sensitive forecasting, offering a replicable approach for advertising allocation and scheduling decisions in competitive media environments.
EfficientNetB4–Vision Transformer Fusion for Chili Leaf Disease Classification Using Multi-Source Datasets Reza Putri Angga; Wahyu Syaifullah Jauharis Saputra; Alfan Rizaldy Pratama
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3753

Abstract

Chili plants are a commodity susceptible to plant pest organism attacks that can significantly reduce productivity. Visual identification of chili diseases by farmers is often inaccurate due to symptom similarity across disease categories, necessitating a technology-based approach capable of performing classification automatically and accurately. This study proposes a hybrid model combining EfficientNetB4 and Vision Transformer for chili leaf disease classification into four categories healthy, yellowish, curl leaf, and spot leaf. EfficientNetB4 extracts local features through compound scaling and MBConv blocks, while ViT models global relationships among image regions through self-attention, enabling a semantically meaningful integration of local and global feature representations that addresses the individual limitations of CNN and transformer-based architectures. The dataset integrates 4,000 secondary images from GitHub and 800 primary images collected directly from chili cultivation fields in Central Java, with splitting performed separately per source to ensure proportional distribution across subsets. To evaluate generalization capability, the model was assessed across three scenarios: training and testing on secondary data only 98.25%, testing on primary field data without prior field exposure 87.50%, and training and testing on integrated data 99.17%, with a perfect accuracy of 100% on the primary-only test set. These results demonstrate that incorporating field-collected data into training directly bridges the generalization gap caused by domain shift between laboratory and real-world conditions, outperforming both single-architecture and previous hybrid approaches reported in prior studies. The findings provide a methodological foundation for developing robust automated disease detection systems applicable across diverse agricultural crops and real-world farming environments.
Co-Authors Abdillah, Indah Rahma Agus Arifin, Agus Agus Zainal Arifin Ahmad Adiib Aminullah Alfan Rizaldy Pratama Alfath Daryl Alhajir Alyssa Amorita Azzah Amelia, Meisya Vira Amellia Harmaimun Hidayah Aminullah, Ahmad Adiib Amri Muhaimin Andry Syva Maldini Anniswa, Iqbal Ramadhan Anny Yuniarti Aurora Prameswaty, Almira Aviolla Terza Damaliana Aviolla Terza Damaliana Aziz, Rizky Dandi Nur Faizi Diana Novitasari Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Dwi Arman Prasetya Eva Yulia Puspaningrum Faris Nur Tsani Firza Prima Aditiawan Gunawan, Ellexia Leonie Hanif Assalmi, Fityan Herdianti, Rahmalia Anindya I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa idhom, Mohammad Indira Iqbal Ramadhan Anniswa Kartika Maulida Hindrayani Kemal Fahreza Jibran Jibran Made Hanindia Prami Swari Mahardhika, Rivaldi Dwi Maulana, Hendra Maulidya Prastita Syah Maurisa Arimbi Putri Meisya Vira Amelia Mohammad Idhom Mohammad Idhom Muhaimin, Amri Muhammad Hakam Fardana Naomi Dwi Anggraini Nashrullah, Ahmad Firqi Ningrum, Lisya Septyo Parlika, Rizky Prismahardi Aji Riyantoko Putra, Chrystia Aji Putri, Milla Akbarany Bakhtiar Renaldy Al Ikhsan Reza Putri Angga Riswanda, Mohammad Nizar Rizkiyah, Selly Rusdiyanto, Nur Rahmat Safira Kusmindasari, Anggun Salsabila, Nada Shindi Shella May Wara Sinthya Putri, Diana Steffany Marcellia Witanto Suprapto, Claudia Millennia Susrama Mas Diyasa, I Gede Taruna Ardianto Tresna Maulana Fahrudin Trimono Trimono Trimono, Trimono Yisti Vita Via Zahrah, Fathima