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All Journal Telematika JUITA : Jurnal Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Creative Information Technology Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi INTECOMS: Journal of Information Technology and Computer Science METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JURIKOM (Jurnal Riset Komputer) Journal on Education CCIT (Creative Communication and Innovative Technology) Journal Jurnal Tecnoscienza JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Infotek : Jurnal Informatika dan Teknologi Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan EXPLORE Indonesian Journal Computer Science (ijcs) JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) Intechno Journal : Information Technology Journal Explore Jurnal Pengabdian Masyarakat (JURDIMAS) INOVTEK Polbeng - Seri Informatika
Anggit Dwi Hartanto
Universitas AMIKOM Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Mapping of Food Crop Commodity Production Areas in Indonesia Using The Average Linkage Method Hery Priandoko; Alva Hendi Muhammad; Anggit Dwi Hartanto
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 9 No. 2 (2024)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v9i2.219

Abstract

Indonesia consists of several regions that have the potential to meet food needs. One of the main sectors that meet food needs is the agricultural sector. The agricultural sector is a sector that needs significant attention from the central and regional governments in meeting national food needs. Food needs are currently often scarce so people find it difficult to obtain these food needs. The problem of dependence on food needs can endanger the availability of the country's food supply. Importing food crop commodities is one solution to maintaining food availability in Indonesia. Imports of food crop commodities carried out by Indonesia show that the amount of food commodity availability cannot meet national food needs. In Indonesia, some regions have food crop commodity production so that they can help in the availability of these food needs. From the existing problems, researchers tried to conduct research by mapping the regions or areas in Indonesia to find out which regions have food crop commodity production. In this study, the mapping that will be used is using the hierarchical cluster method. The hierarchical cluster method that will be used is the agglomerative hierarchical cluster method with the average linkage method. The results of this study will be formed into 3 clusters with the following details: high cluster, medium cluster, and low cluster. The highest cluster obtained 2 members, namely the Provinces of East Java and Central Java. The Medium Cluster obtained 1 member, namely the Province of West Java. The Low Cluster obtained 31 members, namely the Provinces of Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Bangka Belitung Islands, Riau Islands, DKI Jakarta, DI. Yogyakarta, Banten, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, East Kalimantan, North Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo, West Sulawesi, Maluku, North Maluku, West Papua, and Papua.
A Multispectral YOLOv8-Based System for Real-Time Object Detection and Distance Estimation in Blind Navigation Ema Utami; Erwin Syahrudin; Anggit Dwi Hartanto; Suwanto Raharjo
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.28373

Abstract

Developing reliable real-time navigation systems for visually impaired individuals remains challenging, particularly in dynamic and low-light environments. This study proposes an integrated framework combining YOLOv8, OpenCV-based monocular distance estimation, and RGB–NIR multispectral imaging to enhance detection robustness and distance awareness. A dataset of 1,700 annotated images collected from diverse indoor and outdoor environments was used for training and evaluation using preprocessing techniques such as resizing, normalization, and data augmentation. System performance was evaluated using Precision, Recall, F1-Score, mean Average Precision (mAP), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Frames Per Second (FPS). Experimental results show that YOLOv8x achieved the best performance with an F1-Score of 0.91, mAP@50 of 0.74, MAE of 0.15 m, RMSE of 0.20 m, and a processing speed of 22 FPS. Multispectral RGB–NIR integration further improved low-light performance, increasing the F1-Score from 0.83 to 0.89 and reducing MAE from 0.28 m to 0.19 m with only a minor reduction in speed. These findings demonstrate that the proposed system provides an effective balance between accuracy and real-time performance for assistive navigation applications.
Stock Price Prediction Using SVR: A Feature Engineering and Hyperparameter Tuning Approach Alfian Ramadhan; Yoga Pristyanto; Anggit Dwi Hartanto; Donni Prabowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7249

Abstract

Stock price prediction in Indonesia's volatile mining sector poses significant forecasting challenges driven by commodity price dynamics and structural market shifts. This study proposes a systematic prediction framework for PT Indo Tambangraya Megah Tbk (ITMG.JK) integrating technical and market-derived non-technical feature engineering, LightGBM-based feature selection, multilevel TimeSeriesSplit cross-validation, and hyperparameter optimization. Support Vector Regression (SVR) is benchmarked against LightGBM, XGBoost, and Random Forest under 5-fold, 10-fold, and 15-fold schemes. SVR achieves the best performance at 10-fold, with RMSE of 0.0121, MAE of 0.0090, MAPE of 1.1457%, and R² of 0.9249. Generalization experiments across four additional stocks in banking, automotive, and mining sectors confirm SVR's robustness, maintaining R² above 0.89 and MAPE below 2.65% in all cases while tree-based models produce negative R² on certain datasets. Statistical validation via Wilcoxon signed-rank test (p < 0.05) and Cohen's d (|d| > 0.8) confirms the significance of SVR's advantage. These findings indicate that SVR consistently outperforms the evaluated models under the proposed experimental framework.
Pemanfaatan Sistem Informasi sebagai Pendukung Integrasi Data Atik Nurmasani -; Sharazita Dyah Anggita; Eli Pujastuti; Ika Asti Astuti; Anggit Dwi Hartanto
JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) Vol. 2 No. 3 (2024): JITER-PM
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jiter-pm.v2i3.6357

Abstract

Utilization of technology in an institution can be done with many alternatives. One of them is through the use of information systems to support business processes. Data management and data archiving have different provisions and methods between work units and data is owned by each work unit. This difference results in data not being integrated and difficulty finding data when needed. The solution is that archiving and data management is carried out centrally through a website information system to support business processes. The method used consists of analyzing collaborators needs, creating an information system, demoing an information system, installing an information system, and evaluating the use of an information system. The formulated requirements can be used as a basis for creating an information system as a data integration center. The result of information system is demonstrated to collaborators to obtain feedback. The final result of the information system needs to be installed to be installed online so it can be used by collaborators to get feedback of the use the information system. The features in the available information system can help work units archive and manage data easily. Yayasan admins can access work unit data online as needed.
Optimization of Stock Trading Strategies Using a Hybrid Reinforcement Learning and Forecasting Model Rezha Ikhwan Hidayat; Anggit Dwi Hartanto
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: 10.35314/9vzmbf06

Abstract

Stock price prediction is an interesting challenge in machine learning due to the non-linear nature of the market. Although forecasting models can predict prices, they often do not provide optimal trading strategies. Reinforcement learning (RL) has the potential to optimize strategies, but it is highly dependent on the input states. This study integrates two methods—a CNN-LSTM forecasting model and RL (A3C)—to develop an algorithmic trading strategy. The model is evaluated using historical INDF stock data (2016–2024) with a data-split validation protocol of 80% training and 20% testing. Backtesting simulations on the period (Feb 2023–Dec 2024) show that the hybrid model achieves a cumulative total return of 121.44%. This result was obtained using an all-in trading strategy (one full position at a time) and includes transaction costs: a trading fee of 0.01% per transaction and a borrow interest rate of 0.0003% per day for short positions. This performance significantly outperforms traditional strategies: Buy and Hold (23.45%), MA Crossover (51.13%), RSI (9.09%), and MACD (−29.08%). The hybrid model also achieves a Sharpe Ratio of 2.381 (annualized, assuming a 0% risk-free rate).
Literatur Review Bat Algorithm Terhadap Analisis Sentimen Pada Lini Masa Twitter Candra Adipradana; Ema Utami; Anggit Dwi Hartanto
JURNAL TECNOSCIENZA Vol. 5 No. 1 (2020): JURNAL TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/v4mwd237

Abstract

Algoritma metaheuristik seperti particle swarm optimization, firefly algorithm and harmony sekarang menjadi metode yang kuat untuk menyelesaikan banyak masalah optimasi yang sulit. Dalam literature review ini, kami mengusulkan suatu metode metaheuristik baru yaitu Binary Bat Algorithm atau Algoritma Kelelawar dengan Biner, hal ini didasarkan pada perilaku ekolokasi kelelawar. Kami juga berniat untuk menggabungkan keunggulan dari algoritma yang ada ke dalam algoritma kelelawar baru. Setelah perumusan terperinci dan penjelasan implementasinya, kami akan melakukannya perbandingan algoritma yang diusulkan dengan algoritma lain yang ada, termasuk genetic algorithms and particle swarm optimization. Simulasi menunjukkan bahwa algoritma yang diusulkan tampaknya jauh lebih unggul daripada algoritma lainnya, dan kedepannya studi lebih lanjut juga akan dibahas. Kata kunci: Biner, Ekolokasi, Metaheuristik, Algoritma Kelelawar
Klasifikasi Kepribadian Dengan Metode DISC Pada Twitter Menggunakan Algoritma Artificial Neural Network Idris Idris; Ema Utami; Anggit Dwi Hartanto
JURNAL TECNOSCIENZA Vol. 5 No. 1 (2020): JURNAL TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/3s308g79

Abstract

Maju mundurnya suatu perusahaan biasanya didukung oleh adanya sumber daya yang handal, terutama sumber daya manusia. Perekrutan dan penempatan pegawai pada posisi yang tepat akan membawa dampak yang signifikan bagi suatu perusahaan. Di dunia ini sifat dan karakter manusia sangat beraneka ragam bentuknya. Teori DISC mengklasifikasikan kepribadian menjadi empat tipe yaitu dominance, influence, steadiness dan compliance. Perbedaan karakter setiap tipe tentu saja akan berpengaruh pada gaya perilaku, cara menghadapi tekanan hidup dan juga cara berkomunikasi baik secara langsung maupun dengan media sosial. Melalui sosial media, seseorang dapat meluapkan perasaanya melalui postingan yang diunggahnya. Dari postingan tersebut dapat dilakukan analisis mengenai karakter kepribadian yang ia dimiliki. Penelitian ini bertujuan untuk mengetahui seberapa besar akurasi analisis profiling pada Twitter sehingga bisa menjadi acuan untuk proses perekrutan pegawai. Penelitian ini menggunakan algoritma Artificial Neural Network untuk mengklasifikasikan 275 akun Twitter kedalam teori DISC dan mendapatkan akurasi sebesar 42,91% dari 72 skenario yang dijalankan. Kata kunci: Kepribadian DISC, Media Sosial, Analisis Profiling, Sumber Daya Manusia
Evaluating YOLOv8-Based Distance Estimation: A Comparison of OpenCV and Coordinate Attention Weighting in Blind Navigation Systems Erwin Syahrudin; Ema Utami; Anggit Dwi Hartanto; Suwanto Raharjo
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24395

Abstract

Background: Recent developments in assistive technologies for the visually impaired have increasingly utilized computer vision techniques for real-time distance estimation. However, challenges remain in balancing accuracy, latency, and robustness under dynamic environmental conditions. Objective: This study aimed to evaluate and compare the performance of OpenCV and Coordinate Attention Weighting (CAW) models for distance estimation in blind navigation systems, particularly focusing on their effectiveness in real-time scenarios. Methods: A quantitative experimental study was conducted using an image dataset labeled with actual distances. The baseline performances of OpenCV and CAW were measured and compared. Subsequently, targeted optimizations were applied to the OpenCV model, including adaptive image filtering, hyperparameter tuning, and integration of a Kalman filter. Results: Initial evaluation showed that CAW achieved a higher baseline accuracy of 88% compared to OpenCV. However, after optimizations, OpenCV’s accuracy improved by 15%, reaching approximately 85%. Additionally, the optimized OpenCV model demonstrated reduced latency, outperforming CAW in real-time detection speed. Under varying lighting and motion conditions, OpenCV also exhibited superior robustness compared to CAW. Conclusion: The findings suggest that with proper optimization, OpenCV can match or exceed CAW in key performance aspects, making it a viable and efficient alternative for real-time distance estimation in blind navigation systems. Future research should explore further model integration and hardware acceleration for deployment in wearable devices.
Implementasi Algoritma Naïve Bayes Dalam Mengidentifikasi Jenis Penyakit Cacar Dengan Image Processing Yudha Bagas Pattimura; Melcior Paitin Kanoena; Anggit Dwi Hartanto; Hartatik Hartatik; Kusnawi Kusnawi
Intechno Journal : Information Technology Journal Vol. 5 No. 1 (2023): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2023v5i1.1571

Abstract

Cacar merupakan salah satu penyakit kulit yang sering diderita banyak masyarakat mulai dari anak bayi sampai orang tua. Cacar memliki beberapa jenis yang antara lain adalah cacar air (Quipperian), cacar api (herpes zoster) dan cacar monyet, seluruh penyakit ini semuanya dapat menular ke seama manusia melalui kontak lansung, bersin, batuk atau tersentuh dengan isi gelembung cacar yang pecah. Minimnya pengetahuan masyarakat dan tidak adanya penyuluahan dari pemerintah membuat masyarakat tidak mengetahui akan perbedaan jenis-jenis cacar yang diderita dan dapat terjadinya kesalahan dalam pengobatan. Dalam penilitian ini kami menggunakan image processing dengan metode histogram untuk ekstraksi fitur tekstur cacar tersebut serta menggunakan dengan metode klasifikasi naïve bayes dalam mengklasifikasi jenis cacar yang diderita oleh pasien. Dari penilitian yang kami lakukan menunjukan bahwa mengklasifikasi nilai ekstraksi fitur tekstur citra cacar dengan metode naïve bayes memperolehonilai akurasi sebesar 75%.
Co-Authors Ahmad Fauzi Akhmad Efendi Al Abdul Rohim Alfian Ramadhan Alva Hendi Muhammad Alvina Felicia Watratan Anggita, Sharazita Dyah Anik Sri Widowati Atik Nurmasani - Biva Candra Lutfi Adiatma Bobby Candera Lim Candra Adipradana Deas Pradana Donni Prabowo Edy Hardianto Rifeni Eli Pujastuti Ema Utami Eny Nurnilawati Erwin Syahrudin Fajar Surya Putro Fakhrur Wiradhika Halim Bayuaji Sumarna Hanafi Hanafi Hartatik Hery Priandoko Idris Idris Ika Asti Astuti Ivan Rifky Hendrawan Jangkung Tri Nygroho Kusnawi Kusnawi Kusrini Melcior Paitin Kanoena Muhammad Ilham Fadila Muhammad Ridlo Arifandi Mulia Sulistiyono Omar Muhammad Altoumi Alsyaibani Pamela Hapsari Putri Rezha Ikhwan Hidayat Rumini Rumini Suwanto Raharjo Taufiq Nashrullah Triana Triana Wahyu Hidayat wahyuni, wenti ayu Winarnie Yoga Pristyanto Yudha Bagas Pattimura