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All Journal TEKNIK INFORMATIKA Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) Faktor Exacta SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang JSiI (Jurnal Sistem Informasi) Petir Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) jurnal syntax admiration Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) International Journal Software Engineering and Computer Science (IJSECS) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer KOMMAS: Jurnal Pengabdian Kepada Masyarakat SENTRI: Jurnal Riset Ilmiah SAINSTECH: Jurnal Penelitian dan Pengkajian Sains dan Teknologi Community: Jurnal Pengabdian Pada Masyarakat Jurnal Ilmu Komputer (JIK) OKTAL : Jurnal Ilmu Komputer dan Sains AMMA : Jurnal Pengabdian Masyarakat Jurnal Intelek Insan Cendikia Journal of Informatics and Vocational Education
Yan Mitha Djaksana
Universitas Pamulang
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Social Sciences

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Transformers in Machine Learning: Literature Review Thoyyibah T; Wasis Haryono; Achmad Udin Zailani; Yan Mitha Djaksana; Neny Rosmawarni; Nunik Destria Arianti
Jurnal Penelitian Pendidikan IPA Vol 9 No 9 (2023): September
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i9.5040

Abstract

In this study, the researcher presents an approach regarding methods in Transformer Machine Learning. Initially, transformers are neural network architectures that are considered as inputs. Transformers are widely used in various studies with various objects. The transformer is one of the deep learning architectures that can be modified. Transformers are also mechanisms that study contextual relationships between words. Transformers are used for text compression in readings. Transformers are used to recognize chemical images with an accuracy rate of 96%. Transformers are used to detect a person's emotions. Transformer to detect emotions in social media conversations, for example, on Facebook with happy, sad, and angry categories. Figure 1 illustrates the encoder and decoder process through the input process and produces output. the purpose of this study is to only review literature from various journals that discuss transformers. This explanation is also done by presenting the subject or dataset, data analysis method, year, and accuracy achieved. By using the methods presented, researchers can conclude results in search of the highest accuracy and opportunities for further research.
Penguatan Literasi Digital Siswa melalui Kampanye Bijak Bermedia Sosial: Pencegahan Cyberbullying dan Hoaks Dahlan Supriatna; Yan Mitha Djaksana; Joko Suwarno; Romdhoni Setiawan; Donny Aulia; Sri Sulasmi; Muhamad Tahir Retob
Community : Jurnal Pengabdian Pada Masyarakat Vol. 6 No. 2 (2026): Juli: Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/v43fra48

Abstract

Kegiatan Pengabdian Kepada Masyarakat (PKM) ini bertujuan untuk menguatkan literasi digital siswa SMK melalui kampanye bijak bermedia sosial dalam pencegahan cyberbullying dan hoaks. Rendahnya literasi digital menyebabkan siswa rentan menjadi korban maupun pelaku cyberbullying serta mudah terpapar misinformasi. Metode yang digunakan meliputi sosialisasi interaktif, workshop praktis identifikasi hoaks, simulasi penanganan cyberbullying, dan kampanye digital partisipatif. Kegiatan dilaksanakan di SMKS An Nurmaniyah, Tangerang, dengan melibatkan 40 siswa. Hasil pre-post test menunjukkan peningkatan signifikan: pengetahuan cyberbullying meningkat 60,9% (dari 40,8 menjadi 65,7), kemampuan identifikasi hoaks meningkat 70,8% (dari 39,5 menjadi 67,5), sikap empati digital meningkat 40,4%, dan perilaku cyberbullying menurun 38,6%. Tingkat kepuasan peserta mencapai 100% dan 95% siswa menyatakan minat menjadi Digital Ambassador. Kegiatan ini membuktikan bahwa pendekatan partisipatif efektif dalam membangun kesadaran kritis dan perilaku bertanggung jawab di ruang digital.
One-Hour-Ahead Mean Radiant Temperature Forecasting in Jabodetabek Using CNN-LSTM and Temporal Convolutional Networks Novana Sari; Tukiyat Tukiyat; Yan Mitha Djaksana
Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i3.3802

Abstract

Mean Radiant Temperature (MRT) represents the combined shortwave and longwave radiant load experienced by a human body, but continuous observations are rarely available across large metropolitan areas. This study developed and compared a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model and a Temporal Convolutional Network (TCN) for one-hour-ahead MRT forecasting in Jabodetabek. The dataset comprised 210,528 hourly records from 2023–2024 at 12 ERA5 grid points. Five radiation variables and two near-surface thermal variables were used as predictors, while ERA5-HEAT MRT was the target. Each sample contained a 24-hour historical window. Three chronological train-validation-test splits were evaluated: 80:10:10, 70:10:20, and 70:15:15. Both architectures achieved R² values above 0.93 in all scenarios. Under the 80:10:10 split, TCN produced the lowest RMSE value of 2.8104 °C, the highest R² value of 0.9518, the lowest validation loss value of 5.9592, and a residual bias of −0.4863 °C. CNN-LSTM achieved the lowest MAE value of 1.8874 °C and MAPE value of 5.76% and was more stable when the training proportion decreased. Overall, TCN 80:10:10 was selected as the best configuration, although field validation is required before operational deployment.
Predicting Solar Radiation Using Temporal Convolutional Networks and Long Short-Term Memory: A Comparative Study Based on Automatic Weather Station Data Famiyana Dewi; Tukiyat; Yan Mitha Djaksana
Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i3.3758

Abstract

Solar radiation is a key meteorological parameter for climatology, hydrology, agriculture, renewable energy, and environmental analysis. Direct measurement remains limited because it requires specialised instruments, regular calibration, and high maintenance costs. This study compared Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) models for predicting solar radiation using Automatic Weather Station data from the Aceh Climatological Station. The dataset comprised 4,152 observations from January to December 2023, with rainfall, air temperature, relative humidity, air pressure, wind speed, wind direction, and time features as predictors, while solar radiation served as the target variable. The research process included data quality control, interpolation of invalid values, Min-Max Scaling, time feature engineering, sliding-window sequence generation with 24 time steps, and time-based data splitting using 70:15:15, 80:10:10, and 70:10:20 scenarios. Model performance was evaluated using MAE, RMSE, MAPE, and R². The 80:10:10 split produced the best results for both models. The LSTM model achieved slightly better overall performance than TCN based on RMSE and R², although both models captured the temporal pattern of solar radiation. The study provides an AWS-based temporal deep learning framework for radiation prediction in tropical regions of Indonesia.
Comparative Analysis of LSTM and CNN–LSTM Models for Daily Air Temperature Prediction in Tanjung Priok Port Using Multivariate Meteorological Data Erian Tasa; Tukiyat Tukiyat; Yan Mitha Djaksana
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7564

Abstract

Air temperature prediction is important for supporting weather monitoring and operational activities in port areas. Accurate temperature forecasting can support decision-making related to maritime transportation, logistics, and weather-based risk mitigation. This study compares the performance of Long Short-Term Memory (LSTM) and Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) models for daily air temperature prediction in the Tanjung Priok Port area. The dataset consists of daily meteorological observations collected from the Tanjung Priok Maritime Meteorological Station, Indonesia, covering the period from 2000 to 2025. Eight input variables were used, including rainfall, sunshine duration, air pressure, average humidity, average wind speed, and three lagged temperature variables. Data preprocessing included data cleaning, 7-day moving average smoothing, MinMaxScaler normalization, and sequence generation using a sliding-window approach. The dataset was divided into training (70%), validation (15%), and testing (15%) sets. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The results show that the LSTM model achieved the best overall performance, with an MAE of 0.1457 °C, RMSE of 0.1811 °C, MAPE of 0.5030%, and R² of 0.9423. In comparison, the CNN–LSTM model obtained an MAE of 0.2566 °C, RMSE of 0.3316 °C, MAPE of 0.8802%, and R² of 0.8065. These results indicate that the standalone LSTM model performed better than the CNN–LSTM model in predicting daily air temperature for the Tanjung Priok Port dataset.
Analysis of Early Detection of Automatic Weather Station Sensor Failures: A Comparative Study of Supervised Deep Neural Networks and Unsupervised Autoencoders Hasbullah Zuhri Hasibuan; Sudarno Wiharjo; Yan Mitha Djaksana
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7593

Abstract

Automatic Weather Stations (AWS) continuously collect and record meteorological data in real time and support weather information services. The reliability of AWS observations depends on sensor performance, as sensor failures, equipment degradation, and communication disturbances may produce anomalous data and reduce data quality. This study aims to develop an early-detection approach for AWS sensor anomalies using a Deep Neural Network (DNN) and an Autoencoder, compare their performance, and identify the sensors most frequently associated with anomalies. The dataset consists of 385,237 observations collected from the AWS Ancol station in North Jakarta from January to September 2025, covering nine meteorological and oceanographic parameters. The study involved data preprocessing, Min-Max normalization, model training, and evaluation using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC. The DNN achieved 99.6% accuracy, 0.922 precision, 0.996 recall, 0.958 F1-score, and 1.000 AUC. The Autoencoder achieved 95.8% accuracy, 0.578 precision, 0.023 recall, 0.043 F1-score, and 0.584 AUC. The AUC of 1.000 obtained by the DNN should be interpreted within the characteristics of the labeled dataset used in this study and should not be considered evidence of perfect generalization. Sensor analysis identified wind direction as the parameter most frequently associated with anomalies, contributing 46.67% of the detected anomalies in the DNN and 66.02% in the Autoencoder. These findings indicate that the supervised DNN performed better than the Autoencoder for anomaly detection on the labeled AWS dataset used in this study.
Co-Authors Achmad Udin Zailani Agus Buono Dahlan Supriatna Donny Aulia Erian Tasa Famiyana Dewi Habib Nurfaizal Hardiansyah Hardiansyah Haryono, Wasis Hasan Sajili Hasbullah Zuhri Hasibuan Hay's, Riyan Naufal Hendra Rahmawan Hendra Rahmawan Heru Sukoco I GNM Dharma Putra I Made Sugi Ardana Ikwan Jihansyah, Muhamad Joko Suwarno Kelvin Gunawan Lisda Fitriana Masitoh Lisda Fitriana Masitoh M. Irwan Makhsun Makhsun Muhamad Abdurahman Kamil Muhamad Tahir Retob Muhammad Hendry Muhammad Sulaiman Neny Rosmawarni Nopriyandi , Muhammad Teguh Novana Sari Nunik Destria Arianti Nyoman Sugi Widiastithi, Ni Octaviana Anugrah Ade Purnama Romdhoni Setiawan Sasqia Zufriani Shelvie Nidya Neyman Siswanto Sri Damayanti Sri Sulasmi Sri Wahjuni Sudarno Wiharjo Sudarno Wiharjo Thoyyibah T Thoyyibah T Tukiyat Tukiyat Tukiyat Tukiyat Tukiyat Ubaedi, Ibnu Usman Maulana Riyansyah Weni Gurita Aedi Weni Gurita Aedi