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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah KOMPUTASI JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jiko (Jurnal Informatika dan komputer) Jurnal Riset Informatika Jurnal Sistem informasi dan informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) Jifosi Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Teknik Informatika (JUTIF) Djtechno: Jurnal Teknologi Informasi International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Sosial dan Abdimas Journal of Information Systems and Technology Research Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Terapan (J-TETA) Journal of Community Service (JCOS) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
idhom, Mohammad
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Religion Humanities Automotive Engineering 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Clustering of the Air Pollution Standard Index (ISPU) in the Province of DKI Jakarta Using the CLARANS Algorithm Azzahra, Adelia Ramadhina; Nabila, Nasywa Azzah; Idhom, Mohammad; Trimono, Trimono
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9783

Abstract

Air pollution has become a serious global issue. According to IQAir's 2024 report, DKI Jakarta ranked 10th among cities with the worst air quality worldwide, indicating that air pollution in DKI Jakarta has reached a concerning level. This research uses the CLARANS algorithm to cluster daily air quality in DKI Jakarta based on pollution parameters. CLARANS is chosen due to its advantages in terms of big data processing efficiency, outlier resistance, and medoid search capability. The novelty of this research lies in the application of CLARANS to overcome the limitations of clustering algorithms in previous research. This research comprises several stages, including data understanding, data preprocessing, building the CLARANS model, and evaluation using the silhouette score. The CLARANS clustering result using the most optimal parameter combination and k = 3 demonstrates well-separated cluster boundaries, with an overall average silhouette score across all regions and years of 0.6398. The analysis results indicate that air pollution in DKI Jakarta tends to worsen in 2024. Jakarta Barat and Jakarta Pusat are predominantly affected by PM10, CO, and O₃ pollution, whereas Jakarta Selatan and Jakarta Utara are more influenced by SO₂ and NO₂ pollution. On the other hand, air pollution in East Jakarta shows a balanced dominance from both pollutant categories.
Application of CNN-BiLSTM Algorithm for Ethereum Price Prediction Diash, Hakam Dzakwan; Nathania, Vannesa; Idhom, Mohammad; Trimono, Trimono
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9757

Abstract

The volatile and dynamic Ethereum (ETH) market demands an accurate predictive model to support investment decision making. The complexity of ETH time series data and the influence of various external factors make price prediction a challenge in itself. This study aims to develop an ETH price prediction model using a combined architecture of Convolutional Neural Network (CNN) and also Bidirectional Long Short-Term Memory (BiLSTM). CNN is used to extract local features from historical ETH closing price data, while BiLSTM models bidirectional temporal patterns. The dataset used includes ETH daily price from January 2020 to January 2025, which are obtained from Yahoo Finance and have gone through a normalization process and transformation into sequential form. The model is trained for 100 epochs with an early stopping mechanism to prevent overfitting and evaluated using the MAPE and coefficient of determination (R²) metrics. The evaluation results show that the CNN-BiLSTM model is able to predict ETH prices with a MAPE value of 2.8546% and an R² of 0.9415, indicating high performance in capturing actual data trends. This study shows that the hybrid CNN-BiLSTM approach is effective for Ethereum price prediction.
Integrating IndoBERTweet and GRU for Opinion Classification on X Towards Public Transportation in Jakarta Nafiah, Fajria Ulumin; Panglima, Talitha Fujisai; Idhom, Mohammad; Trimono, Trimono
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10723

Abstract

Jakarta, the capital of Indonesia, faces persistent challenges with its public transportation system due to rapid urbanization, increased use of private vehicles, and poor service quality. While social media platforms such as X (formerly Twitter) offer valuable insights into public opinion, their unstructured nature complicates analysis. This study uses deep learning models to categorize user sentiments into six labels that cover positive and negative aspects of comfort, safety, and punctuality. The results show that IndoBERTweet achieved the highest performance, with 95.43% accuracy and a macro F1-score of 0.9545. It also required the shortest training time, at six minutes and 30 seconds. IndoBERTweet+GRU followed closely behind with an accuracy of 94.62% and a macro F1-score of 0.9460 in six minutes and 50 seconds. This shows that adding a GRU layer provides competitive results, but does not surpass the baseline model. Error analysis revealed that, while the models performed well with explicit sentiments, the models struggled with implicit expressions, such as sarcasm and mixed opinions. These results demonstrate the potential of sentiment analysis in real-time monitoring systems, which could help policymakers identify urgent issues and support data-driven improvements in Jakarta’s urban transportation services.
Co-Authors Alif, Rahmat Istighfaroni Aminullah, Ahmad Adiib Angga, Angga Rahmad Purnama Anggraini Puspita Sari Anniswa, Iqbal Ramadhan Aviolla Terza Damaliana Azzahra, Adelia Ramadhina Bajramaya, Dewa Widya Basuki Rahmat Masdi Siduppa Cahaya Purtri Agustika Carissa, Savvy Prissy Amellia Damaliana, Aviolla Terza Diash, Hakam Dzakwan Dwi Arman Prasetya Fahrudin, Tresna Maulana Halim, Rahman Nur Harahap, Jasmine Avrile Kaniasari Henni Endah Wahanani Hindrayani, Kartika Maulida Jauharis Saputra, Wahyu Syaifullah JS, Wahyu Syaifullah Khasanah, Ema Isfa'atin Kristiawan, Kiki Yuniar Kurniawati, Dyah Ayu Listyo Lidya Musaffak, Awal Linggasari, Dienna Eries Lisanthoni, Angela Maulana, Hendra Maulida Hindrayani, Kartika Muhammad Rizki Alamsyah Mutiara Irmadhani Nabila, Nasywa Azzah Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nathania, Vannesa naufal firdaus, ahmad Pamungkas, Syahrul Ardi Panglima, Talitha Fujisai Permadani, Citra Amelia Intan Priananda, Arya Mahardika Putri, Deva Amalia Rahma Raynaldi, Achmad Riyantoko, Prismahardi Aji Rizaldy Pratama, Alfan Ryan Dana, Alvin Saputra, Wahyu Syaifullah Jauharis Shaffa Ameera, Divanda Sugiarti, Nova Putri Dwi Susrama Mas Diyasa, I Gede Syaifullah JS, Wahyu Terza Damaliana, Aviolla Thohir, A. Zaki Thoriqulhaq, Muhammad Trimono, Trimono Wardana, Azel Christian Wardani, Firly Setya Widi Saputro, Tegar Windyadari, Chrysilla Citra