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SISTEM INFORMASI PENERIMAAN SISWA BARU (PSB) ONLINE PADA SMA NEGERI 2 TARUSAN DENGAN PHP DAN MYSQL Andi, Tri; Nugraha, Hafiz
Jurnal EDik Informatika Vol 7, No 1 (2020)
Publisher : STKIP PGRI Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22202/ei.2020.v7i1.4265

SMA Negeri 2 is Tarusan a secondary school in the Pesisir Selatan regency. Every new beginning academic year in the school, will be held the selection of new admissions. In the execution of these activities often face problems because the system used is still manual. With the New Student Reception Information System at SMAN 2 Online Tarusan expected to help deliver information and facilitate the execution of the processing and management of data for new students. The method uses in this research is interviewing, observation, literature study, analysis, system design, testing, and implementation. This research is expected of information systems created to provide the ease of implementation of a new student at SMAN 2 Tarusan.Keywords : Information Systems, Information Systems Admission.

Exploring the search trends: Inter Milan's triumph in Indonesia Laksana, Eko Pramudya; Fajaruddin, Syarief; Andi, Tri; Hudha, Muhammad Nur
Sepakbola Vol 4, No 1 (2024)
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/sepakbola.v4i1.287

This article explores the popularity of Inter Milan in Indonesia through an in-depth analysis of Google search trends. Data from Google Trends between May 2023 and May 2024 was collected and analyzed using a rigorous qualitative methodology with an ethnographic approach. The study focuses on two main metrics: interest over time and regional distribution per province. The results show Inter Milan's search volume and regional interest fluctuate, with a notable peak during the UEFA Champions League final against Manchester City on June 11, 2023. The research illuminates the multifaceted and enthusiastic fan base of Inter Milan and the club's dedication to social initiatives and community engagement.

Stock price forecasting using Long Short Term Memory Andi, Tri; Kusuma, Candra Juni Cahya
Internet of Things and Artificial Intelligence Journal Vol. 5 No. 1 (2025): Volume 5 Issue 1, 2025 [February]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v5i1.900

The objective of this research is to develop a solution for predicting BRI stock prices using Long Short-Term Memory (LSTM) models. The LSTM model was selected for its capacity to process extensive time series data and discern latent temporal patterns. In this study, a BRI stock dataset obtained from Yahoo Finance is employed for the training and testing of an LSTM model. The evaluation results demonstrate that the LSTM model exhibits excellent predictive performance, with a mean absolute percentage error (MAPE) of 1.58768% and a root mean square error (RMSE) of 81.88216%. The Google test results demonstrate a low mean absolute percentage error (MAPE) of 1.5%, indicating a strong correlation between the predicted and true values. In other words, the RMSE values indicate the absolute error level in predictions, indicating the extent to which the model performs well when predicting a value that takes into account the context of the data. In conclusion, the proposed LSTM model shows promise for use in stock price prediction applications. The precision of these models can be tested by using them to make predictions, which would validate the decision-making supported by data. This research suggests that there is room for improvement of these models using techniques such as hyperparameter optimization or ensemble methods (bagging with other weak learners, etc.) to improve their accuracy.

Exploring the search trends: Inter Milan's triumph in Indonesia Laksana, Eko Pramudya; Fajaruddin, Syarief; Andi, Tri; Hudha, Muhammad Nur
Sepakbola Vol. 4 No. 1 (2024)
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/sepakbola.v4i1.287

This article explores the popularity of Inter Milan in Indonesia through an in-depth analysis of Google search trends. Data from Google Trends between May 2023 and May 2024 was collected and analyzed using a rigorous qualitative methodology with an ethnographic approach. The study focuses on two main metrics: interest over time and regional distribution per province. The results show Inter Milan's search volume and regional interest fluctuate, with a notable peak during the UEFA Champions League final against Manchester City on June 11, 2023. The research illuminates the multifaceted and enthusiastic fan base of Inter Milan and the club's dedication to social initiatives and community engagement.

Cluster Based Classification of River Water Pollution Using K-Means for Policy Intervention and Environmental Justice in Central Java, Indonesia Andi, Tri; Lu’ay Khoironi , Moh.; Kusuma , Candra Juni Cahyo; Khairunnisa, Khairunnisa
Journal of Law, Environmental and Justice Vol. 3 No. 2 (2025): Journal of Law, Environmental and Justice
Publisher : Ius et Ambientis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62264/jlej.v3i2.132

The lack of technical parameters for water pollution clustering exacerbates the fragmentation of authority, weak supervision, and disharmony between regions, making it necessary to normalize classifications in technical regulations to ensure standardization and adequate ecology. This research aims to develop a policy design for classifying river water pollution in accordance with environmental justice theory. This type of research employs empirical legal research approach with a statistical focus on environmental regulations, utilizing case studies from several cities/districts in Indonesia and Central Java Province as samples for factual analysis. This research shows, first, that the issue of river pollution in Indonesia reveals a weak effectiveness of regulations and governance, thereby urging the implementation of an environmental justice framework based on polluter clustering according to regional typology characteristics. Second, the clustering results obtained using the K-Means method are divided into three clusters: Cluster 0, Cluster 1, and Cluster 2. Third, this research recommends the design of a river pollution classification policy based on environmental justice theory, which demands the normative standardization of pollution clusters in the Regulation of the Minister of Environment and Forestry, in order to create a formal legal instrument.

Pelatihan penggunaan Sistem Informasi Himpunan Disabilitas Muhammadiyah Andi, Tri; Syahrul, Syahrul; Fujiastuti, Ariesty; Firmansyah, Muhammad Adifa
ABDIMAS DEWANTARA Vol 8 No 1 (2025)
Publisher : Universitas Sarjanawiyata Tamansiswa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30738/ad.v8i1.19623

Artikel ini membahas pelatihan penggunaan Sistem Informasi Himpunan Disabilitas Muhammadiyah (HIDIMU) sebagai upaya strategis dalam mendukung transformasi digital pengelolaan data penyandang disabilitas. Masalah utama yang melatarbelakangi pengembangan sistem ini adalah ketiadaan basis data terpusat yang valid dan akurat, serta keterbatasan kemampuan teknis pengurus dalam mengelola data secara digital. Penelitian ini bertujuan untuk mendeskripsikan proses pelatihan, mengevaluasi pemahaman peserta, dan meninjau efektivitas sistem informasi HIDIMU dalam pengelolaan data berbasis komunitas. Metode pelatihan dilakukan secara partisipatif dan berbasis praktik langsung, melibatkan 15 peserta dari pengurus dan admin Himpuan Disabilitas Muhammadiyah wilayah Yogyakarta. Hasil menunjukkan peningkatan signifikan dalam kemampuan peserta dalam mengoperasikan fitur sistem seperti Data Management dan User Management. Sistem ini memfasilitasi pemantauan dan rekapitulasi data secara real-time, serta mendukung proses pengambilan keputusan yang lebih akurat. Namun, ditemukan beberapa tantangan, seperti keterbatasan aksesibilitas bagi pengguna dengan gangguan penglihatan dan keterbatasan infrastruktur internet. Simpulan dari kegiatan ini menunjukkan bahwa pelatihan sistem informasi HIDIMU tidak hanya meningkatkan kapasitas teknis individu, tetapi juga memperkuat semangat kolaborasi dan pemberdayaan komunitas. Artikel ini merekomendasikan pengembangan lanjutan sistem yang inklusif serta perluasan implementasi ke wilayah lain sebagai model praktik baik dalam pengelolaan data disabilitas berbasis teknologi. Training on Information System usage of Muhammadiyah Disability Association Abstract: This article discusses training on the use of the Information System of the Muhammadiyah Disability Association (HIDIMU) as a strategic effort to support the digital transformation of data management of persons with disabilities. The main problem behind the development of this system is the absence of a centralized database that is valid and accurate, as well as the limited technical capabilities of administrators in managing data digitally. This study aims to describe the training process, evaluate participants' understanding, and review the effectiveness of the HIDIMU information system in community-based data management. The training method was participatory and hands-on based, involving 15 participants from the administrators and admins of HIMPUAN DISABILITAS MUHAMMADIYAH in the Yogyakarta area. Results showed significant improvement in participants' ability to operate system features such as Data Management and User Management. The system facilitates real-time data monitoring and recapitulation, and supports a more accurate decision-making process. However, some challenges were found, such as limited accessibility for users with visual impairments and limited internet infrastructure. The conclusion of this activity shows that the HIDIMU information system training not only improves the technical capacity of individuals, but also strengthens the spirit of collaboration and community empowerment. This article recommends further development of an inclusive system and expansion of implementation to other areas as a model of good practice in technology-based disability data management.

Deep Learning Architecture for Stock Price Prediction andi, tri; Andriyani, Widyastuti; Purnomosidi D.P, Bambang
Journal of Intelligent Software Systems Vol 3, No 1 (2024): July 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i1.1343

Dalam dunia investasi saham, kemampuan memprediksi pergerakan harga saham secara akurat sangatlah penting. Dua permasalahan utama yang menjadi fokus penelitian ini adalah, bagaimana pemodelan N-BEATS dibandingkan LSTM dan ARIMA pada harga saham Bank BCA, dan bagaimana hasil peramalan model N-BEATS, LSTM, dan ARIMA pada harga saham Bank BCA. Data saham Bank BCA. Untuk menjawab hal tersebut, penelitian ini membahas tentang pengembangan dan evaluasi model peramalan time series N-BEATS. Namun hasil analisis menunjukkan bahwa model ARIMA menunjukkan kinerja yang unggul, dengan pencapaian MAPE sebesar 0,001% pada data menit, 0,006% pada data jam, dan 0,018% pada data hari. Keunggulan ini signifikan dibandingkan model N-BEATS dan LSTM. Oleh karena itu, model ARIMA menunjukkan potensi besar untuk digunakan dalam peramalan deret waktu keuangan, penilaian risiko, dan pemodelan oleh analis keuangan.

Geographic-Origin Music Classification from Numerical Audio Features: Integrating Unsupervised Clustering with Supervised Models Pranolo, Andri; Sularso, Sularso; Anwar, Nuril; Putra, Agung Bella Utama; Wibawa, Aji Prasetya; Saifullah, Shoffan; Dreżewski, Rafał; Nuryana, Zalik; Andi, Tri
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.13400

Classifying the geographic origin of music is a relevant task in music information retrieval, yet most studies have focused on genre or style recognition rather than regional origin. This study evaluates Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models on the UCI Geographical Origin of Music dataset (1,059 tracks from 33 non-Western regions) using numerical audio features. To incorporate latent structure, we first applied K-means clustering with the optimal number of clusters (k=2) determined by the Elbow and Silhouette methods. The cluster assignments were used as auxiliary signals for training, while evaluation relied on the true region labels. Classification performance was assessed with Accuracy, Precision, Recall, and F1-score. Results show that SVM achieved 99.53% accuracy (95% CI: 97.38–99.92%), while CNN reached 98.58% accuracy (95% CI: 95.92–99.52%); Precision, Recall, and F1 mirrored these values. The differences confirm SVM’s superior performance on this dataset, though the near-perfect scores also suggest strong separability in the feature space and potential risks of overfitting. Learning-curve analysis indicated stable training, and cluster supervision provided small but consistent benefits. Overall, SVM remains a reliable baseline for tabular music features, while CNNs may require spectro-temporal representations to leverage their full potential. Future work should validate these findings across multiple datasets, apply cross-validation with statistical significance testing, and explore hybrid deep models for broader generalization.

CPSO-LSTM: Chaotic Particle Swarm Optimization improved LSTM Hyperparameters for Air Pollution Prediction Andi, Tri; Pranolo, Andri; Ismail, Amelia Ritahani; Kusuma, Candra Juni Cahyo
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1689

Accurate air pollution predictions are crucial for public health and environmental management, but achieving high prediction accuracy remains a challenge due to the complexity of temporal patterns in pollution data. This study aims to improve performance of Long Short-Term Memory (LSTM) by optimizing hyperparameters tuning based Chaotic Particle Swarm Optimization (CPSO) for air pollution predictions. Hyperparameter optimization included the number of hidden layers, neurons, activation functions, loss functions, optimizers, batch sizes, and epochs. The proposed model LSTM-CPSO compared to other models, baseline LSTM and PSO-LSTM, to predict the concentrations of PM2.5, PM10, NO2, SO2, CO, and O3 in Jakarta based on Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The experimental results show that CPSO-LSTM achieves superior performance with MSE 0.012105, MAE 0.086356, RMSE 0.110022, and MAPE 32.31%, outperforming the baseline LSTM by 38.1% on the MSE metric and 11.9% on MAPE. Interestingly, LSTM-CPSO produces better architecture with 2 hidden layers and 91 neurons than LSTM-PSO that requires 7 hidden layers with 51 neurons. Similarly, LSTM-CPSO has shortest 5 training epochs better than LSTM-PSO with 16 epochs. This research demonstrates that chaos-based metaheuristic optimization can select the best hyperparameters to improve the performance of LSTM for air quality forecasting.

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