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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Media Statistika Jurnal Studi Manajemen Organisasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah KOMPUTASI BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Jiko (Jurnal Informatika dan komputer) JURNAL PENDIDIKAN TAMBUSAI Jurnal Pendidikan dan Konseling Jurnal Sistem informasi dan informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) International Journal of Advances in Data and Information Systems Al-Mutharahah: Jurnal Penelitian dan Kajian Sosial Keagamaan Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) International Journal of Community Service International Journal of Data Science, Engineering, and Analytics (IJDASEA) Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Inkofar Kreatifitas: Jurnal Ilmiah Pendidikan Islam Bhakti Nagori (Jurnal Pengabdian kepada Masyarakat) Jurnal Ilmiah Edutic : Pendidikan dan Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Eksponensial Baitul Hikmah: Jurnal Ilmiah Keislaman Kohesi: Jurnal Sains dan Teknologi ITIJ Seminar Nasional Teknologi dan Multidisiplin Ilmu Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal ilmiah teknologi informasi Asia
Trimono, Trimono
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Prediction of Rice Harvesting During the Rainy Season in Kabupaten Lamongan Using Stochastic Frontier Analysis Ningrum, Imelda Widya; Prasetya, Dwi Arman; Trimono, Trimono; Kassim, Anuar bin Mohamed
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1393

Abstract

The agricultural sector plays a critical role in ensuring national food security, yet it faces challenges in achieving technical efficiency due to limited land and input resources. This study aims to model and predict the technical efficiency of rice production in Lamongan Regency during the rainy season using a data science-driven Stochastic Frontier Analysis (SFA) approach. The dataset includes key inputs such as land area, labor, fertilizer, and environmental variables. The methodology involved data preprocessing, feature selection based on Pearson correlation and VIF thresholds, and model validation using metrics like R-squared, MAPE, and log-likelihood. The SFA model demonstrated high predictive capability, with R² values exceeding 0.91 in cross-validation and MAPE under 15%. The low gamma value (? = 0.0100) indicates minimal yet consistent inefficiency. The results suggest that integrating SFA with data science techniques provides an effective framework for identifying inefficiencies and can serve as a decision-support system for evidence-based agricultural policy.
Identifying Academic Excellence: Fuzzy Subtractive Clustering of Student Learning Outcomes Wibowo, Muhammad Bagas Satrio; Hindrayani, Kartika Maulida; Trimono, Trimono
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4614

Abstract

Education forms a vital foundation for a nation's future. In this digital era, while the use of Information and Communication Technology (ICT) in education is increasing, it brings increasingly complex challenges in education data management and analysis. The growing number of students each year results in a large volume of data, which would be difficult to manage if still relying on manual methods. Manual approaches are inefficient, time-consuming, prone to inconsistencies and human error, especially when identifying outstanding students in large and complex data. This research aims to implement a clustering system to group outstanding students at XYZ elementary school using the Fuzzy Subtractive Clustering (FSC) method. FSC was chosen for its ability to identify data groups based on the density of data points. FSC involves several important parameters, including radius, squash factor, acceptance ratio, and rejection ratio. Added variabel of social and spiritual values aims to enhance grouping quality by offering a broader perspective on students' character, attitudes, and social interactions. Parameter exploration shows an increase in the silhouette score from 0.20–0.45 to 0.45-0.57 and variable addition spiritual and social values, which indicates clearer cluster separation and provides better insights. The best parameters results were achieved with radius 0.3, accept ratio 0.5, reject ratio 0.04, and squash factor 1.25, resulting in a Silhouette Score of 0.57 and forming 5 student groups. Cluster results can guide special mentoring for students with low academic, spiritual, and social values, and support personalized learning programs based on each cluster’s characteristics.
Stock Price Prediction and Risk Estimation Using Hybrid CNN-LSTM and VaR-ECF Febriyanti, Alvi Yuana; Prasetya, Dwi Arman; Trimono, Trimono
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4648

Abstract

Stock price prediction is a major challenge in the financial domain due to high volatility and complex movement patterns. Traditional methods such as fundamental and technical analysis often fail to capture the non-linear characteristics and fast-changing market dynamics, highlighting the need for more adaptive approaches. This study proposes a hybrid deep learning model, CNN-LSTM, which combines CNN's local feature extraction capabilities with LSTM’s ability to model long-term temporal dependencies. To incorporate risk management, the model is also integrated with the Value at Risk (VaR) approach using the Cornish-Fisher Expansion (ECF) to estimate potential losses under extreme market conditions. The study utilizes daily historical stock price data of PT Unilever Indonesia Tbk retrieved from Yahoo Finance. Model performance is evaluated using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), where the model achieves an MAE of 78.13 and a MAPE of 2.72%, indicating relatively low absolute and relative prediction errors. These results confirm that the CNN-LSTM approach effectively models stock price movements in dynamic market environments, and the integration with VaR-ECF provides a more comprehensive risk estimate. Thus, this approach not only enhances predictive accuracy but also offers valuable decision-support tools for investors in planning investment strategies.
ANALISIS SEGMENTASI PENUMPANG TRANSJAKARTA BERDASARKAN POLA WAKTU, RUTE, DAN JUMLAH PEMBAYARAN MENGGUNAKAN PCA DAN K-MEANS CLUSTERING Arifta, Septia Dini; Irawan, Tanaya Anindita; Maulana Pasha, Naufal Ricko; Trimono, Trimono; Nasrudin, Muhammad
Jurnal Inkofar Vol 9, No 1 (2025)
Publisher : Politeknik META Industri Cikarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46846/jurnalinkofar.v9i1.437

Abstract

Transportasi publik memiliki peran strategis dalam mendukung mobilitas masyarakat di kawasan urban, terutama di kota metropolitan seperti Jakarta yang memiliki tingkat kepadatan lalu lintas tinggi. Untuk meningkatkan efisiensi operasional serta kualitas layanan, penyedia layanan transportasi perlu memahami karakteristik dan pola perilaku penumpang secara lebih mendalam. Penelitian ini bertujuan untuk melakukan segmentasi penumpang TransJakarta berdasarkan pola waktu perjalanan, rute perjalanan, dan jumlah pembayaran yang dilakukan. Metode yang digunakan dalam penelitian ini adalah Principal Component Analysis (PCA) untuk mereduksi dimensi data dan mengekstraksi fitur utama, serta K-Means Clustering untuk mengelompokkan penumpang ke dalam klaster yang memiliki kesamaan karakteristik. Analisis Elbow Method digunakan untuk menentukan jumlah klaster optimal, yang dalam hal ini adalah tiga klaster. Klaster pertama terdiri dari penumpang yang melakukan perjalanan jauh dengan jumlah pembayaran tinggi. Klaster kedua mencakup penumpang yang cenderung melakukan perjalanan pendek dengan jumlah pembayaran yang rendah, dan klaster ketiga merupakan penumpang dengan karakteristik perjalanan menengah dari segi jarak dan pembayaran. Segmentasi ini memberikan wawasan penting yang dapat digunakan oleh pengelola layanan TransJakarta dalam menetapkan kebijakan strategis, seperti perencanaan rute yang lebih efisien, penyesuaian tarif berdasarkan segmentasi pengguna, serta penyusunan strategi pemasaran yang lebih tepat sasaran. Hasil penelitian ini menunjukkan bahwa pendekatan berbasis data dapat menjadi alat yang efektif untuk mendukung pengambilan keputusan dalam pengelolaan sistem transportasi publik yang adaptif dan berkelanjutan.
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.
Pendidik dalam Perspektif Hadis dari Kata Al Tarbiyah Rizal, Syamsul; Trimono, Trimono
Baitul Hikmah: Jurnal Ilmiah Keislaman Vol 3 No 1 (2025): Baitul Hikmah: Jurnal Ilmiah Keislaman
Publisher : Pascasarjana IAI Diniyyah Pekanbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46781/baitul_hikmah.v3i1.1588

Abstract

Penelitian ini bertujuan untuk mengkaji konsep pendidik dalam perspektif hadits dengan fokus pada kata al-tarbiyah. Pendekatan kualitatif digunakan dalam penelitian ini dengan metode kajian pustaka dan analisis konten. Hadits sebagai salah satu sumber ajaran Islam memberikan panduan yang komprehensif terkait peran dan tanggung jawab pendidik. Kata al-tarbiyah yang sering disebut dalam hadits memiliki makna yang dalam, mencakup pendidikan moral, etika, dan pengembangan karakter. Penelitian ini menemukan bahwa hadits mendorong pendidik untuk tidak hanya mengajar ilmu pengetahuan, tetapi juga membentuk kepribadian dan karakter peserta didik sesuai dengan nilai-nilai Islam. Dengan demikian, pendidik dalam perspektif hadits memiliki tanggung jawab yang luas, mencakup aspek akademis, moral, dan spiritual.
SENTIMENT ANALYSIS THE DAMAGE ESAF FRAME WITH SUPPORT VECTOR MACHINE AND IMPACT ON HONDA MOTORCYCLE SALES Putri Ariyani, Kinanthi; Terza Damaliana, Aviolla; Trimono, Trimono
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/m0kyc955

Abstract

Damage to the Enhanced Smart Architecture Frame (eSAF) on Honda motorcycles has triggered consumer concerns and has become a public spotlight. This study analyzes public sentiment towards the problem using the Support Vector Machine (SVM) and its impact on sales at one of the dealerships in Surabaya. The data used was in the form of comments from Twitter social media which were classified into two classes, namely positive and negative. Based on the results of the analysis, the majority of 589 public sentiments (59.7%) tended to be negative towards the problem of damage to the eSAF frame, while 397 public sentiments (40.3%) showed positive sentiment. Sales results showed significant fluctuations after this issue emerged, along with increasing negative sentiment. SVM models with a Linear kernel provide the best results with 85% accuracy, 84% precision, 85% recall, and 85% f1-score. SVM was chosen because it excels in text classification compared to algorithms such as K-Nearest Neighbors (KNN), C4.5, and Naïve Bayes, and has been applied in areas such as face detection, bioinformatics, and text processing. This research provides insights for manufacturers to improve product quality, improve customer service, and restore public trust. In addition, the use of the Support Vector Machine algorithm in sentiment analysis can be a reference for similar research in other fields.
ENHANCED CLUSTERING USING PSO-KMEDOIDS FOR GOVERNMENT AID DISTRIBUTION Fitriani, Aulia Nur; Hindrayani, Kartika Maulida; Trimono, Trimono
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/gegxdv17

Abstract

The distribution of social assistance in Indonesia often experiences problems due to inaccuracies in recipient data between those recorded in government systems and field conditions. In Kalipuro Village, Mojokerto District, data mismatches caused difficulties in screening assistance, requiring village officials to manually re-filter the data. This triggered protests from citizens who should have received assistance but did not get their rights. To overcome this problem, this research proposes the use of the K-Medoids algorithm which is able to overcome sensitivity to outliers. This algorithm is used to cluster data based on criteria such as occupation, number of assets, number of dependents, and income. In addition, this research incorporates the Particle Swarm Optimization (PSO) technique to optimise the clustering process, which is expected to improve accuracy and efficiency in social assistance distribution. The results of clustering analysis using the K-Medoids algorithm show that the best cluster is obtained at the number of clusters K=5, with the distribution of cluster 0 (179 households), cluster 1(89 households), cluster 2 (296 households), cluster 3 (354 households), and cluster 4 (94 households). The Silhouette Score value of 0.6531 indicates good cohesion and separation between clusters. Based on the analysis, cluster 1 is the top priority group of aid recipients, followed by clusters 4, 2, 3, and 0. The K-Medoids algorithm effectively identifies the most needy community groups, supporting targeted and efficient decisions in aid distribution.
IMPLEMENTATION OF KERNEL COMBINATION GAUSSIAN PROCESS REGRESSOR IN LOYALTY PREDICTION (CASE STUDY: ONLINE MOTORCYCLE TAXI) Aziziyah, Luqna; Prasetya, Dwi Arman; Trimono, Trimono
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/nm9b4w40

Abstract

In the application-based transportation industry, customer loyalty is a crucial factor affecting service sustainability. This study aims to analyze and predict customer loyalty in online motorcycle taxi services in Surabaya using the Gaussian Process Regressor (GPR) with a kernel combination approach. Data were collected through a survey of 467 students from public universities in Surabaya, considering service quality, price, and innovation factors. The analysis process includes data processing, validation, cleaning, and modeling using Gaussian Process Regression techniques. The results indicate that the kernel combination in GPR effectively captures complex non-linear patterns in survey data, with low Root Mean Squared Error (RMSE) and R² values close to 1. These findings suggest that the proposed approach can provide accurate customer loyalty predictions. This study contributes to developing strategies for online motorcycle taxi service providers to enhance user experience and maintain market share. The findings highlight the importance of applying machine learning models to understand customer behavior and support data-driven business decision-making.
Co-Authors Abdullah Abdullah Ade Irma Agustian Adiwidyatma, Afdhal Reshanda Aliya Dasa Pramesthi Amanillah, Rahmatul Ardiani, Ardia Eva Arif, Farah Yusnaida Arifta, Septia Dini Aurelia, Cenditya Ayu Aviolla Terza Damaliana Awang, Wan Suryani Wan Aziziyah, Luqna Azni Aisyah Azzahra, Adelia Ramadhina Bainar Bainar, Bainar Bey Lirna, Cagiva Chaedar Carissa, Savvy Prissy Amellia Damaliana, Aviolla Terza Dewi, Ni Luh Ayu Nariswari Di Asih I Maruddani Di Asih I Maruddani Diash, Hakam Dzakwan Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Fahrudin, Tresna Maulana Fairuz Luthfia Winoto Putri, Maretta Febri Giantara Febriyanti, Alvi Yuana Febyanti, Iin Fitriani, Aulia Nur Hadi, Surjo Hadiyan Pradipta, Alvino Hasan Herlina Herlina Hervrizal, Hervrizal Hindrayani, Kartika Maulida I Gede Susrama Mas Diyasa Icha Rohmatul Jannah idhom, Mohammad Ikaningtyas, Maharani Irawan, Tanaya Anindita Kartini Kartini Kassim, Anuar bin Mohamed Khairunisa, Adenda Khosyi, Hanun Aufa Nur Kusdani, Kusdani Kuswardana, Dendy Arizki Linggasari, Dienna Eries Lisanthoni, Angela Maruddani, Di Asih Mas Diyasa, I Gede Susrama Mas Diyasa, I Gede Susrama Susrama Maulana Pasha, Naufal Ricko Maulidiyyah, Nova Auliyatul Mohammad Idhom Muhaimin, Amri Muhammad Nasrudin Nabila, Nasywa Azzah Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nasution, Baktiar Nathania, Vannesa Ningrum, Imelda Widya Ningtiyas, Rona Wulan Novita Anggraini Nugraheni, Setiawati Oktaviani, Sheny Eka Panglima, Talitha Fujisai Prisma Hardi Aji Riyantoko Putra, Andrawana Putri Ariyani, Kinanthi Putri, Irma Amanda Putri, Nabila Rizky Amalia Rafiqah, Lailan Rafli Feandika Nugroho, Muhammad Ratna Yulistiani Renaldi, Sahat Rhomaningtias, Lina Riswanda, Mohammad Nizar Riyantoko, Prismahardi Aji Ryan Dana, Alvin Sabela, Sefilah Naurah Safira Devi, Arsita Safira, Alya Mirza Salma Namira, Alivia Saputra, Wahyu Syaifullah Jauharis Sihananto, Andreas Sonhaji, Abdulah Sugiarti, Nova Putri Dwi Suprapto, Rheinka Elyana Susrama Mas Diyasa , I Gede Syamsul Rizal Syukri Syukri Tarno Tarno Taufik, Ikbar Athallah Terza Damaliana, Aviolla Utami, Rianti Siswi Utriweni Mukhaiyar Valentina, Tiara Wardah Ariij Adibah Wardah, Salsabila Wibowo, Muhammad Bagas Satrio Widayawati, Eny Widison, Daffin Tanjiro Yuciana Wilandari yuliza, eva Zalfa Assyadida, Azizah