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All Journal JADECS (Journal of Art, Design, Art Education and Culture Studies) Jurnal Penelitian Pendidikan IPA (JPPIPA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF)
Al zami, Farrikh
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Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Economics, Econometrics & Finance Education Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Social Sciences

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GUIDELINES OF MOOD – THINKING – LOGIC PROFILING & ANTI-HOAX FRAMEWORK: DETECTING SOMEONE'S MOTIVES ON SOCIAL MEDIA Gamayanto, Indra; wibowo, sasono; Novianto, Sendi; Al zami, Farrikh; Sirait, Tamsir Hasudungan; sani, Ramadhan rakhmat
JADECS (Journal of Art, Design, Art Education & Cultural Studies) Vol 6, No 2 (2021)
Publisher : Jurusan Seni dan Desain, Fakultas Sastra, Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um037v6i22021p80-102

Abstract

Abstract—Social media is a lifestyle, starting from how to think and behave towards something. Understanding what is on social media requires a systematic guide to distinguish between true and false information. Therefore, this article will answer it. Two important parts of this article are discussing mood-thinking-logic which is the basis of every human's thinking, which then results in two attitudes, namely doing the right or wrong thing. This article complements the two articles that have been published. Because the problem regarding hoaxes is still an unfinished debate and still has problems finding the right formula or guide, in this article we create two concepts to solve this problem. the first concept produces guidelines of mood-thinking-logic profiling, which are concepts for understanding the layers of feelings, thoughts and logic of a person and the motives he does in social media, then the second concept is anti-hoax framework which discusses seven levels of hoaxes and solutions to overcome hoaxes. Both of these concepts will be accompanied by examples of case studies that discuss these matters, so that readers will understand the two concepts. Furthermore, this research is still being developed because it still needs a lot of refinement, and this research is part of the text mining research that we are currently doing.Keywords—Mood, Thinking, Logic, Profiling, Anti Hoax
RETRACTED : Pelatihan Game Design Untuk Siswa SD Pada Pusat Kegiatan Belajar Masyarakat (PKBM) Semarang Gamayanto, Indra; Wibowo, Sasono; Novianto, Sendi; Al Zami, Farrikh; Sundjaja, Arta Moro; Sirait, Tamsir Hasudungan
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 2 (2024): MEI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i2.2251

Abstract

Artikel dengan judul Pelatihan Game Design Untuk Siswa SD Pada Pusat Kegiatan Belajar Masyarakat (PKBM) Semarang telah dilakukan pencabutan dari jurnal Abdimasku Vol. 7 No. 2 Mei 2024, pada tautan daring https://abdimasku.lppm.dinus.ac.id/index.php/jurnalabdimasku/article/view/2251. Hal ini dikarenakan kesamaan judul artikel pada https://pubmas.umus.ac.id/index.php/devozione/article/view/935.
Optimizing Chronic Kidney Disease Diagnosis Using the C4.5 Algorithm and Missing Value Imputation Strategies Riyanto, Ahmad; Purwanto, Purwanto; Al Zami, Farrikh; Andreuw Meda, Ridodio
Jurnal Penelitian Pendidikan IPA Vol 11 No 9 (2025): September
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i9.12456

Abstract

The occurrence of missing values in data mining is a significant challenge that can hinder the knowledge extraction process. Incomplete data not only reduces efficiency in data management and analysis, but also has the potential to bias decision-making. This study aims to improve the performance of the C4.5 algorithm in dealing with missing value problems through the application of imputation techniques and GridSearchCV optimization. In this study, we propose an approach to handling missing values by combining several imputation methods, including minimum, maximum, mean-mode, median, and k-Nearest Neighbors (k-NN). These methods are applied to the Chronic Kidney Disease dataset obtained from the UCI Repository. After the imputation process, we performed hyperparameter optimization using GridSearchCV to find the best parameter combination for the C4.5 algorithm. Experimental results show that the application of imputation techniques and GridSearchCV optimization significantly improves the classification accuracy of the C4.5 algorithm. The comparison results show that the application of missing value handling, combined with GridSearchCV optimization, successfully improves the accuracy of the model by 2.25% compared to without using missing values. This proves that handling missing values along with proper GridSearchCV optimization can improve the prediction quality of the model.
IMPLEMENTATION OF LSTM (LONG SHORT TERM MEMORY) ALGORITHM TO PREDICT WEATHER IN CENTRAL JAVA Irwan, Rhedy; Andono, Pulung Nurtantio; Al Zami, Farrikh; Ocky Saputra, Filmada; Megantara, Rama Aria; Handoko, L. Budi; Umam, Chaerul
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1118

Abstract

Agro-indutrial agricultural production such as red onions in Indonesia has a very important share in driving Indonesia's economic growth, especially in Central Java province which contributed 28.15% of the total national red onion production in 2021. Weather conditions have a major influence on the red onion planting process until the red onions are ready to be harvested. In this study, the objective is to predict various types of weather such as rainfall, air temperature, and air humidity in seven districts in Central Java, namely Brebes, Temanggung, Demak, Boyolali, Kendal, Pati, and Tegal. To do this, the use of the LSTM (Long Short Term Memory) algorithm with its ability to store memory longer than RNN will be reliable for predicting various types of weather in the future. This research was developed with the CRISP-DM (Cross Industry Process Model for Data Mining) method which has a goal-oriented approach, this method is a mature and widely accepted method in Data Mining with various applications in Machine Learning. With the final results from 39 models by using the evaluation of the average value of train MSE 0.013, test RMSE 0.11, test MSE of 0.02, test RMSE 0.12 and succeed to predict 5 days or months ahead from the last data that is provided.
Improving YOLO Performance with Advanced Data Augmentation for Soccer Object Detection Puspita, Rahayuning Febriyanti; Naufal, Muhammad; Al Zami, Farrikh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11256

Abstract

This study developed an object detection system for soccer games using the YOLOv8m algorithm with four main classes: player, goalkeeper, referee, and ball. The dataset, consisting of 372 annotated images, exhibited class imbalance, with significantly fewer ball instances compared to players. The basic YOLOv8m architecture was used without internal modifications, but adjustments were made to the output layer and fine-tuning of the pre-trained weights to adapt to the new dataset. Two models were compared: one without and one with advanced augmentation techniques (mosaic, mixup, cutmix). The experimental results showed an increase in mAP@50 from 74.9% to 81.4% in the augmented model, with a statistically significant difference (p < 0.01). However, model performance still decreased under extreme conditions such as high occlusion, rapid movement, and uneven lighting. The combination of data augmentation, output layer adaptation, and fine-tuning proved effective in improving object detection accuracy and provided the basis for the development of a real-time artificial intelligence-based soccer match analysis system.
Optimized LSTM with TSCV for Forecasting Indonesian Bank Stocks Salsabila, Rizka Mars; Fahmi, Amiq; Al Zami, Farrikh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11314

Abstract

Volatility in financial markets presents complex forecasting challenges for investors, particularly within emerging economies such as Indonesia. This study proposes an optimized Long Short-Term Memory (LSTM) model for forecasting the stock prices of five significant Indonesian banks: BBCA, BBRI, BMRI, BBNI, and BBTN, utilizing daily OHLCV data (Open, High, Low, Close, Volume) and technical indicators from 2020 to 2025. The dataset comprises over 6,000 daily records, segmented using a sliding window approach to preserve temporal structure and enhance learning efficiency. Concurrently, the model architecture comprising dual LSTM layers with dropout regularization was refined through systematic hyperparameter tuning to enhance predictive performance. Model evaluation employed 5-fold Time Series Cross-Validation (TSCV), a sequential validation technique that mitigates data leakage and explicitly overcomes the limitations of conventional k-fold methods by preserving chronological integrity. Performance metrics included MSE, RMSE, MAE, R², and MAPE. The experiment results demonstrate the model’s robustness in capturing long-term dependencies within financial time series. BBCA and BMRI achieved superior accuracy (R² > 0.95), with BBCA recording the lowest MAPE of 2.34%. Despite market fluctuations, the model maintained consistent reliability across all test folds. This study overcomes a methodological limitation by integrating LSTM with TSCV in expanding markets, offering actionable insights for investors, analysts, and policymakers, and serving as a reference for adaptive AI-based, more informed forecasting tools. Moreover, the proposed framework holds promise for broader application across other financial sectors and regional markets with similar volatility characteristics.
Data-Driven K-Means Clustering Analysis for Stunting Risk Profiling of Pregnant Women Nazella, Desvita Dian; Hadi, Heru Pramono; Al Zami, Farrikh; Ashari, Ayu; Kusumawati, Yupie; Suharnawi, Suharnawi; Megantara, Rama Aria; Naufal, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8415

Abstract

Stunting in children is influenced by maternal health conditions during pregnancy. This study aims to classify pregnant women to prevent stunting based on clinical, demographic, and environmental factors using the K-Means Clustering algorithm. A total of 229 data from the Primadona application (Disdalduk KB Kota Semarang) were analyzed using 14 normalized variables. The optimal number of clusters was determined using the Elbow Method and validated using the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. The Kruskal-Wallis test was performed to verify differences between clusters. This study resulted in seven clusters with different profiles, with a Silhouette Score of 0.134, Davies-Bouldin Index of 1.509, and Calinski-Harabasz Index of 29.54. These values ​​indicate that the cluster structure is formed and reflects the variation in risk for pregnant women, although there is overlap due to differences in characteristics between individuals. The clustering successfully differentiated pregnant women with low to high risk, influenced by health and environmental factors. This study proves the effectiveness of K-Means in identifying stunting risk patterns in pregnant women and supports more targeted interventions, such as nutritional counseling, disease risk monitoring, education on cigarette smoke exposure, and referrals. Limitations of this study include the unbalanced distribution of data between and the use of cross-sectional data. Future research is recommended to improve pre-processing and compare other clustering methods such as K-Medoids or DBSCAN for more precise stunting risk analysis.
Co-Authors Affandy Affandy Andreuw Meda, Ridodio Arta Moro Sundjaja, Arta Moro Ashari, Ayu Assaqty, Mohammad Iqbal Saryuddin Chaerul Umam Fahmi Amiq Hadi, Heru Pramono Indra Gamayanto Irwan, Rhedy Kusumawati, Yupie L. Budi Handoko Marjuni, Aris Megantara, Rama Aria Muhammad Naufal, Muhammad Nazella, Desvita Dian Ocky Saputra, Filmada Prasetya, Rudy Eko Pulung Nurtantio Andono Purwanto Purwanto Puspita, Rahayuning Febriyanti Ramadhan Rakhmat Sani Riyanto, Ahmad Salsabila, Rizka Mars Sasono Wibowo Sendi Novianto Sirait, Tamsir Hasudungan Soeleman, M. Arief Suharnawi Suharnawi