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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Madura JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL INSTEK (Informatika Sains dan Teknologi) Prosiding SNITP (Seminar Nasional Inovasi Teknologi Penerbangan) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika bit-Tech Jurnal Teknologi Informatika dan Komputer Jurnal Diferensial Enthusiastic : International Journal of Applied Statistics and Data Science MATHunesa: Jurnal Ilmiah Matematika Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Komputer Indonesia INOVTEK Polbeng - Seri Informatika Prosiding Seminar Nasional Ilmu Teknik
Alfan Rizaldy Pratama
Universitas Pembangunan Nasional Veteran Jawa Timur
Aerospace Engineering Humanities 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 Mathematics Public Health Social Sciences Transportation

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Pengembangan Algoritma Sharpe Ratio dengan Integrasi Filter Tren SMA dalam Strategi Portofolio Aset Kripto Andri Fauzan Adziima; Shindi Shella May Wara; Muhammad Nasrudin; Alfan Rizaldy Pratama
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 1 (2025): Juni 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i1.383

Abstract

Penelitian ini mengusulkan sebuah strategi alokasi aset kripto yang bersifat dinamis, dengan menggabungkan pembobotan berdasarkan Sharpe Ratio dan penyaringan tren menggunakan indikator Simple Moving Average (SMA) dari Bitcoin (BTC). Model ini melakukan alokasi ulang modal setiap tiga hari pada tujuh aset kripto utama (BTC, ETH, BNB, SOL, TON, TRX, XRP), dengan ketentuan bahwa harga BTC berada di atas ambang SMA tertentu (50 hari, 100 hari, atau 200 hari). Apabila BTC berada di bawah nilai SMA tersebut, seluruh portofolio secara otomatis dialihkan ke USDT untuk menekan risiko penurunan nilai. Studi ini menggunakan data historis dari 1 Januari 2024 hingga 1 Januari 2025 dan menguji performa model dalam tiga konfigurasi SMA, lalu dibandingkan dengan strategi dasar buy-and-hold. Hasil menunjukkan bahwa strategi dengan parameter SMA 50 hari menghasilkan return kumulatif tertinggi (+231,51%) serta rasio Sharpe terbaik (2,51), jauh melampaui model dengan SMA yang lebih panjang maupun rata-rata return dari strategi dasar (+132,14%). Analisis risiko mengindikasikan bahwa jendela SMA yang lebih pendek memberikan respons yang lebih cepat terhadap tren naik pasar, meskipun disertai dengan peningkatan volatilitas jangka pendek. Secara keseluruhan, temuan ini menguatkan efektivitas strategi hibrida yang mengombinasikan penyaringan tren dengan alokasi berbasis risiko dalam pengelolaan portofolio kripto di tengah kondisi pasar yang fluktuatif.
Optimasi Sistem Antrian Pada Medical Center ITS Dengan Simulasi Discrete Event Dan Response Surface Methodology Shindi Shella May Wara; Muhammad Nasrudin; Andri Fauzan Adziima; Alfan Rizaldy Pratama
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 1 (2025): Juni 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i1.411

Abstract

Medical Center ITS berfungsi sebagai unit rawat jalan yang melayani pemeriksaan, tindakan medis, penunjang medis, dan rujukan bagi civitas academica ITS serta masyarakat umum dengan biaya yang terjangkau. Penelitian ini bertujuan untuk mengoptimalkan sistem antrean di Medical Center ITS, yang sering menghadapi antrean panjang dan berdampak pada waktu tunggu pasien serta efisiensi pelayanan, menggunakan pendekatan simulasi diskrit. Data primer, meliputi waktu antar kedatangan dan waktu pelayanan (resepsionis, poli umum, poli gigi, resep, dan pengambilan obat), dikumpulkan secara empiris untuk memodelkan sistem antrean berbasis kejadian. Model simulasi yang dikembangkan secara akurat merepresentasikan seluruh alur pelayanan. Hasil simulasi menunjukkan bahwa sistem pelayanan saat ini belum optimal dengan hanya satu server di poli umum dan satu di poli gigi. Berdasarkan temuan, skenario penambahan server pada poli gigi menjadi empat dan tetap satu server di poli umum diusulkan sebagai konfigurasi optimum. Implementasi skenario ini terbukti secara signifikan mengurangi waktu tunggu rata-rata pasien dan meningkatkan tingkat utilitas sumber daya. Penelitian ini menegaskan bahwa simulasi diskrit adalah alat pengambilan keputusan yang efektif untuk meningkatkan kualitas dan efisiensi pelayanan di fasilitas kesehatan.
Improving Palm Oil Production Efficiency through Deep Learning Algorithms for Fruit Ripeness Detection in Digital Images Tsabita Rosyidah Putri; I Gede Susrama Mas Diyasa; Alfan Rizaldy Pratama
Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Vol 19, No 2: May - August 2026
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/pamator.v19i2.33569

Abstract

Oil palm is a strategic commodity in Indonesia, and its production quality is greatly influenced by the ripeness of the fruit at harvest. Manual ripeness determination is still subjective and prone to errors due to variations in worker experience and environmental conditions. Advances in computer vision and deep learning technology offer a more objective and consistent automated solution. This study aims to develop and evaluate a model for detecting the ripeness level of palm oil fruit using the YOLOv12m algorithm based on digital images. The dataset used consists of 3,375 images with three ripeness classes (unripe, semi-ripe, ripe), which are divided into training, validation, and testing data with a ratio of 70:20:10. The model was trained for a maximum of 25 epochs with an early stopping mechanism. The evaluation was conducted using precision, recall, mAP@50, and mAP@50–95 metrics. The results showed excellent performance with precision of 0.958, recall of 0.946, mAP@50 of 0.985, and mAP@50–95 of 0.882. Class-by-class analysis shows the best performance in the raw and ripe classes, while the unripe class still poses challenges due to visual similarities between transition phases. Overall, the YOLOv12m model has proven to be effective and has the potential to be applied as a more objective and efficient harvest decision support system.
Prediksi Kecepatan Angin Menggunakan Gated Recurrent Unit (GRU) dengan Estimasi Ketidakpastian Monte Carlo Dropout pada Data BMKG Tanjung Perak Alvino Hadiyan Pradipta; Muhammad Rafli Feandika Nugroho; Maretta Fairuz Luthfia Winoto Putri; Alfan Rizaldy Pratama; Shindi Shella May Wara; Muhammad Nasrudin
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.491

Abstract

Abstrak: Keterbatasan metode prediksi konvensional dalam memodelkan dependensi temporal dan ketidakpastian prediksi mendorong pengembangan pendekatan berbasis deep learning. Penelitian ini bertujuan mengembangkan model prediksi kecepatan angin menggunakan metode Gated Recurrent Unit (GRU) pada data meteorologi yang berasal dari stasiun pengamatan BMKG Tanjung Perak. Penelitian ini dilakukan karena metode prediksi sebelumnya masih memiliki keterbatasan dalam menangkap pola temporal dan dependensi jangka panjang pada data time series, serta umumnya belum mengakomodasi ketidakpastian hasil prediksi. GRU dipilih karena mampu memodelkan dependensi temporal secara efisien, sedangkan simulasi Monte Carlo digunakan untuk menghasilkan beberapa skenario prediksi dan mengestimasi interval kepercayaan. Data yang digunakan mencakup parameter kecepatan angin dengan interval waktu tertentu. Hasil evaluasi menunjukkan bahwa model menunjukkan performa yang baik untuk memprediksi kecepatan angin secara akurat, dengan nilai MAE sebesar 0,37, RMSE sebesar 0,50, MAPE sebesar 5,78%, dan R² sebesar 0,986. Dengan demikian, model yang dikembangkan dapat menjadi solusi dalam analisis dan peramalan data time series meteorologi secara komprehensif.Kata kunci: Prediksi Kecepatan Angin, Analisis Time Series, Gated Recurrent Unit (GRU), Simulasi Monte Carlo, Data MeteorologiAbstract: The limitations of conventional forecasting methods in modeling temporal dependencies and forecast uncertainty have driven the development of deep learning-based approaches. This study aims to develop a wind speed forecasting model using the Gated Recurrent Unit (GRU) method on meteorological data from the BMKG Tanjung Perak observation station. This study was conducted because previous prediction methods still have limitations in capturing temporal patterns and long-term dependencies in time series data, and generally do not accommodate the uncertainty of prediction results. GRU was chosen because it is capable of modeling temporal dependencies efficiently, while Monte Carlo simulation was used to generate several prediction scenarios and estimate confidence intervals. The data used includes wind speed parameters at specific time intervals. The evaluation results show that the model shows good performance in predicting wind speed accurately, with an MAE of 0.37, an RMSE of 0.50, a MAPE of 5.78%, and an R² of 0.986. Thus, the developed model can serve as a solution for comprehensive analysis and forecasting of meteorological time series data.Keywords: Wind Speed Prediction, Time Series Analysis, Gated Recurrent Unit (GRU), Monte Carlo Simulation, Meteorological Data 
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.
Implementation of Temporal Fusion Transformer (TFT) for Short-Term Sales Prediction of Telkomsel Data Packages in East Java Muhammad Azkiya Akmal; Trimono; Alfan Rizaldy Pratama
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3268

Abstract

The development of the cellular telecommunications industry has driven an increasing demand for fast, stable, and affordable data services. Accurate forecasting of data package sales is a significant challenge for telecommunications operators due to high demand fluctuations and the complexity of time series patterns. This study aims to implement a Temporal Fusion Transformer (TFT) model based on Seasonal-Trend Decomposition using Loess (STL) to predict short-term sales of Telkomsel data packages in East Java. The data used are sales transactions with hourly time resolution from January to June 2024, focusing on the five data packages with the highest transaction volume. The STL method is applied in the pre-processing stage to separate the trend, seasonal, and residual components, which are then used as additional features in the TFT modeling. Model performance is evaluated using Mean Absolute Error (MAE) and Quantile Risk (q-Risk). The results show that the TFT model is able to produce accurate predictions with an MAE value of 3.6941 and an average q-Risk of 0.0808. Furthermore, interpretability analysis revealed that historical sales variables, seasonal components, and calendar variables significantly contributed to the prediction results. These findings indicate that the STL-based TFT approach is effective for short-term sales forecasting and has the potential to support data-driven operational decision-making in the telecommunications sector.
Klasterisasi Kabupaten/Kota di Provinsi Jawa Tengah berdasarkan Komponen Indeks Pembangunan Manusia Menggunakan Metode UMAP dan K-MEANS Difta Alzena Sakhi; Friza Nur Fatmala; Karina Auralia; Alfan Rizaldy Pratama; Aviolla Terza Damaliana
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 02 (2025)
Publisher : Universitas Negeri Surabaya

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

Abstract

Ketimpangan pembangunan manusia di Provinsi Jawa Tengah tetap menjadi tantangan besar yang memerlukan pendekatan berbasis data. Penelitian ini mengelompokkan kabupaten/kota berdasarkan komponen Indeks Pembangunan Manusia (IPM) tahun 2019–2024 menggunakan algoritma K-Means. Variabel yang dianalisis meliputi Usia Harapan Hidup, Harapan Lama Sekolah, Rata-Rata Lama Sekolah, dan Pengeluaran per Kapita. Data diproses melalui tahapan penting, seperti standarisasi, deteksi outlier, dan reduksi dimensi menggunakan Uniform Manifold Approximation and Projection (UMAP), serta penentuan jumlah klaster optimal dengan Elbow Method dan Silhouette Score. Hasil analisis menunjukkan empat klaster optimal dengan nilai Silhouette Score sebesar 0,71, yang mengelompokkan data tahunan seluruh kabupaten/kota ke dalam kelompok-kelompok dengan tingkat pembangunan manusia yang berbeda secara signifikan. Klaster dengan nilai IPM tertinggi terdiri dari kota-kota besar, seperti Semarang, Salatiga, dan Surakarta, sementara klaster terendah mencakup wilayah yang masih menghadapi berbagai kendala dalam aspek kesehatan, pendidikan, dan ekonomi. Visualisasi UMAP membantu interpretasi distribusi klaster dan memberikan masukan strategis bagi kebijakan pembangunan wilayah yang lebih merata. Kata Kunci: klasterisasi, indeks pembangunan manusia, K-Means, UMAP, Jawa Tengah
Underwater Single and Multiple Objects Detection Based on the Combination of YOLOv7-tiny and Visual Feature Enhancement Dewi Mutiara Sari; Bayu Sandi Marta; R. Haryo Dwito Armono; Alfan Rizaldy Pratama; Firmansyah Putra Pratama
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/91b9qn06

Abstract

Breakwater construction in Indonesia frequently employs tetrapods to dissipate wave energy. However, the placement process remains manual, relying on divers to guide crane operators. This approach not only poses safety risks but also limits visibility due to underwater turbidity. While prior research has focused on underwater image enhancement, the integration of tetrapod object detection remains unexplored. This study proposes a combined method of underwater image enhancement and tetrapod object detection to support land-based operator visualization. Auto-level filtering and histogram equalization techniques were applied to enhance image clarity, followed by object detection using the YOLOv7-tiny model. Tetrapod models at a 1:20 scale were used for training and testing. The proposed system achieved a mean average precision (mAP) of 0.95. Evaluation was conducted across 12 scenarios, involving four lighting levels and two water conditions: clear and 45.8% turbidity. The object detection confidence scores were 0.80 without enhancement, 0.85 with histogram equalization, and 0.84 with auto-level filtering. Multiple object detection achieved an accuracy of 88.75%, outperforming previous approaches using YOLOv4-tiny. The results demonstrate the potential of integrating image enhancement and deep learning-based object detection for improving underwater operational safety and placement precision in breakwater construction.
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.
Implementation of a Hybrid TabNet–XGBoost Model Based on Radiosonde Data for Predicting Daily Rainfall Intensity in Surabaya Annabel Gracia Puryani; Aviolla Terza Damaliana; Alfan Rizaldy Pratama
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.v10i4.13767

Abstract

Rainfall prediction plays an important role in supporting hydrometeorological disaster mitigation and weather-related decision-making. However, accurate rainfall prediction remains challenging because atmospheric processes are highly nonlinear and governed by complex interactions among multiple meteorological variables. This study proposes a Hybrid TabNet–XGBoost model for daily rainfall prediction using integrated radiosonde and surface meteorological observations collected at the BMKG Juanda Class I Meteorological Station. The dataset covers the period from 2019 to 2025 and consists of 2,551 daily observations. TabNet was employed to select the fifteen most informative atmospheric variables based on feature importance, while temporal dependencies were incorporated through lag features generated using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analyses. Hyperparameter optimization was performed using Optuna with TimeSeriesSplit cross-validation prior to model training. Experimental results on the testing dataset achieved an RMSE of 19.1347 mm, an MAE of 11.9742 mm, a MAPE of 17.62%, and an R² of 0.0449. The proposed model was able to capture the general temporal pattern of daily rainfall and produced satisfactory predictions under the dominant rainfall conditions represented in the dataset. However, the model exhibited reduced sensitivity to high-intensity rainfall events, resulting in the underestimation of extreme rainfall and a relatively low R² value, primarily due to the imbalanced rainfall distribution and the complexity of rainfall processes. The optimized model was subsequently applied to generate daily rainfall projections for 2026 based on historical atmospheric observations. Since the corresponding observational data were unavailable at the time of this study, these projections should be interpreted as model-based forecasts rather than validated prediction results. Overall, the proposed Hybrid TabNet–XGBoost framework demonstrates the potential of integrating radiosonde and surface meteorological observations for daily rainfall prediction while highlighting the need for additional atmospheric and spatial information to improve the prediction of extreme rainfall events.
Co-Authors Abda Abda Adziima, Andri Fauzan Alvino Hadiyan Pradipta Andri Faudzan Adziima Andri Fauzan Adziima Annabel Gracia Puryani Anniswa, Iqbal Ramadhan Aviolla Terza Damaliana Aviolla Terza Damaliana Azzahra, Melinda Putri Dadet Pramadihanto Dewi Mutiara Sari Difta Alzena Sakhi Dwito Armono, R. Haryo Faris Nur Tsani Fauzan Adziima, Andri Firmansyah Putra Pratama Friza Nur Fatmala Gede Susrama Mas Diyasa, I I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa idhom, Mohammad Iqbal Ramadhan Anniswa Karina Auralia Kristanaya, Mirechelin Maretta Fairuz Luthfia Winoto Putri Marta, Bayu Sandi Maulidya Prastita Syah Mohammad Idhom Muhammad Azkiya Akmal Muhammad Nasrudin Muhammad Nasrudin Muhammad Rafli Feandika Nugroho Nariswari, Naura Ulayya Putra Pratama, Firmansyah R. Haryo Dwito Armono Ramadhan Anniswa, Iqbal Reza Putri Angga Saputra, Wahyu S.J. Sari, Dewi Mutiara Shindi Shella May Wara Susrama Mas Diyasa, I Gede Syah, Maulidya Prastita Syaifullah J. S, Wahyu Trimono Tsabita Rosyidah Putri Wahyu Syaifullah J S Wahyu Syaifullah Jauharis Saputra Zufar Abdullah Rabbani