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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Media Statistika Jurnal Studi Manajemen Organisasi Elkom: Jurnal Elektronika dan Komputer 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 JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Pendidikan dan Konseling bit-Tech 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 Studies in Learning and Teaching Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Jurnal Bisnis Indonesia Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Kolaboratif Sains Al Khidma: Jurnal Pengabdian Masyarakat Jurnal Ilmiah Edutic : Pendidikan dan Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Eksponensial STATISTIKA Kohesi: Jurnal Sains dan Teknologi Information Technology International Journal (ITIJ) Seminar Nasional Teknologi dan Multidisiplin Ilmu Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal ilmiah teknologi informasi Asia RAGAM: Journal of Statistics and Its Application Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Trimono, Trimono
Universitas Pembangunan Nasional Veteran Jawa Timur
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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.
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.
COMPARISON BETWEEN VALUE AT RISK AND ADJUSTED EXPECTED SHORTFALL: A NUMERICAL ANALYSIS Trimono, Trimono; Maruddani, Di Asih
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1347-1358

Abstract

Loss risk is one of the variable that always appears in every kind of investment. On stock asset investments, the characteristics of the risk of loss is uncertain, this means that losses can occur at any time with a value that cannot be determined certainly. From this condition, investors must manage the loss risk appropriately in order to retain investment stability and get optimal profits. One of the important processes in risk management is loss risk forecast. Risk forecast can be done using risk measures. In stock investment, Value at Risk (VaR) is the most widely used risk measure because has a simple model and can be applied to many types of stocks. However, VaR does not satisfy the axiom of subadditivity, thus VaR is not a coherent risk measure. Another risk measure that is coherent and can be used as an alternative to predict loss risk is the Adjusted-Expected Shortfall (Adj-ES). This study aims to compare VaR and Adj-ES through numerical analysis and backtesting test. So we can get reference to conclude the best risk measure for predicting losses on stock investments. The data used in this study are 2022 IDX blue chip i. e EXCL.JK and ICBP.JK from 09/01/21 to 09/09/22. Based on the backtesting test, the violation ratio value for Adj-ES in every violation probability is less than 1 is less than 1. Then, for VaR at 1% violation probability, the violation ratio value is > 1.
Perbandingan Kinerja LSTM dan GA-LSTM dalam Prediksi Curah Hujan Harian sebagai Strategi Mitigasi Bencana Banjir di Jawa Timur Linggasari, Dienna Eries; Idhom, Mohammad; Trimono, Trimono
Jurnal Ilmiah Komputasi Vol. 24 No. 3 (2025): Jurnal Ilmiah Komputasi : Vol. 24 No 3, September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.24.3.3833

Abstract

Curah hujan merupakan salah satu parameter iklim penting yang sangat memengaruhi keseimbangan lingkungan dan kehidupan manusia, khususnya di daerah tropis seperti Surabaya. Variabilitas curah hujan yang tinggi dapat memicu bencana banjir, sehingga prediksi curah hujan yang akurat menjadi langkah penting dalam upaya mitigasi. Namun, karakteristik curah hujan yang bersifat non-linear, musiman, dan mengandung banyak fluktuasi acak menjadikan prediksi ini sebagai tantangan tersendiri. Penelitian ini bertujuan untuk membandingkan performa model Long Short-Term Memory (LSTM) dan LSTM yang dioptimasi dengan algoritma genetika (GA-LSTM) dalam memprediksi curah hujan harian di Surabaya. Data yang digunakan merupakan data curah hujan harian dari BMKG Surabaya selama periode 2020–2024. Metode penelitian mencakup preprocessing data, pembentukan sekuens, pelatihan model LSTM, optimasi hyperparameter menggunakan GA, serta evaluasi model dengan metrik MSE, RMSE, dan MAE. Hasil penelitian menunjukkan bahwa model GA-LSTM memberikan hasil prediksi yang lebih akurat dengan nilai MSE sebesar 0.0060, dibandingkan dengan LSTM standar sebesar 133.33. Performa GA-LSTM yang lebih stabil dalam menangani fluktuasi ekstrem menunjukkan bahwa pendekatan optimasi berbasis evolusi efektif dalam meningkatkan akurasi prediksi deret waktu curah hujan. Hasil ini diharapkan dapat menjadi referensi ilmiah bagi perumusan kebijakan mitigasi banjir berbasis data.
Penyusunan media pembelajaran digital menggunakan bahasa pemrograman R-Shiny 4.3.3 pada jenjang sekolah menengah atas Trimono, Trimono; Ikaningtyas, Maharani; Widayawati, Eny; Riyantoko, Prismahardi Aji; Widison, Daffin Tanjiro; Khosyi, Hanun Aufa Nur
Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) Vol. 6 No. 3 (2025)
Publisher : Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jp2m.v6i3.22899

Abstract

Penerapan Kurikulum Merdeka pada jenjang SMA berorientasi pada pemanfaatan teknologi dalam proses pembelajaran. Namun, fakta yang terjadi menunjukan bahwa penerapan belum berjalan optimal karena keterbatasan perangkat dan kurangnya penguasaan teknologi oleh guru dan murid. Hal tersebut berdampak pada proses transfer materi yang terhambat, hasil belajar yang belum sesuai target sekolah, serta siswa yang mengalami kesulitan dalam mempelajari materi yang diberikan. Untuk mengatasinya, akan disusun aplikasi pembelajaran digital berbasis Graphical User Interface (GUI).  Aplikasi disusun menggunakan bahasa pemrograman R-Shiny 4.3.3 dan terdiri dari dua struktur utama yaitu ui.io dan server.io. Ui.io berisi perintah mengatur tampilan aplikasi dan dijalankan melalui perintah dashboardHeader, dashboardSidebar dan tabsetPanel. Server.io berisi perintah komputasi untuk memperoleh hasil akhir. Perintah yang digunakan meliputi output$contents, renderDataTable, renderPrint, dan renderPlot. Kegiatan ini bertujuan untuk memberikan keahlian kepada guru untuk menciptakan aplikasi pembelajaran digital yang dapat diterapkan pada proses pembelajaran. Melalui uji mean sampel berpasangan, pada tingkat kepercayaan α = 5% diperoleh hasil bahwa terdapat peningkatan yang signifikan dalam hal kemampuan guru dalam menyusun media pembelajaran. Rata-rata kemampuan guru sebelum mengikuti pelatihan adalah 1,21 dan setelah pelatihan adalah 8,34. Luaran utama yang diperoleh adalah aplikasi pembelajaran untuk mata kuliah Matematika, Fisika, Kimia, dan Ekonomi.
The Influence of Region on Reading Habits in Indonesia: RM-MANOVA Analysis of Population Aged 5+ (2018) Febyanti, Iin; Safira Devi, Arsita; Nugraheni, Setiawati; Wardah, Salsabila; Nasrudin, Muhammad; Trimono, Trimono
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i2pp275-286

Abstract

This study explores the reading habits of the Indonesian population aged 5 and above, focusing on differences between urban and rural areas. Using data from the 2018 BPS survey, the research examines the proportion of individuals who engaged in reading various materials in printed and electronic formats over the past seven days. A Repeated Measures Multivariate Analysis of Variance (RM MANOVA) was employed to assess the influence of regional factors on reading behavior. The results indicated significant disparities: urban populations tend to read a broader range of materials such as newspapers, magazines, and scientific texts, while rural populations focused more on textbooks and basic materials. These findings highlight the need for regionally tailored literacy strategies to ensure equitable access to reading resources across Indonesia.
Prediksi Laju Inflasi di Jawa Timur Menggunakan Model N-BEATS dan Optimasi Optuna: Prediction of Inflation Rate in East Java Using the N-BEATS Model and Optuna Optimization Riswanda, Mohammad Nizar; Trimono, Trimono; Saputra, Wahyu Syaifullah Jauharis
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2141

Abstract

Inflasi merupakan indikator penting yang memengaruhi kestabilan dan pertumbuhan ekonomi suatu wilayah. Prediksi inflasi yang akurat sangat dibutuhkan guna mendukung perumusan kebijakan ekonomi yang tepat. Penelitian ini mengusulkan penggunaan model N-BEATS (Neural Basis Expansion Analysis for Time Series) yang dioptimalkan dengan Optuna untuk memprediksi inflasi di Provinsi Jawa Timur. Data yang digunakan berupa deret waktu univariat, yaitu laju inflasi bulanan dari Januari 2005 hingga Desember 2024, yang diperoleh dari Badan Pusat Statistik (BPS). Evaluasi performa model dilakukan menggunakan metrik Mean Absolute Percentage Error (MAPE). Berbeda dengan model tradisional seperti ARIMA dan LSTM, N-BEATS mengandalkan jaringan saraf feedforward dengan arsitektur blok residual yang mampu melakukan rekonstruksi masa lalu (backcast) dan prediksi masa depan (forecast). Optimasi hyperparameter melalui Optuna berhasil meningkatkan akurasi model secara signifikan. Hasil Penelitian menunjukkan bahwa N-BEATS teroptimasi mencapai MAPE sebesar 8,97%, lebih baik dibandingkan N-BEATS dasar (11,05%), ARIMA (16,95%), dan LSTM (12,23%). Temuan ini mengindikasikan bahwa pendekatan N-BEATS dengan Optuna efektif dalam meningkatkan akurasi prediksi inflasi dan dapat menjadi alat bantu penting bagi perencanaan ekonomi di tingkat daerah.
Pengaruh Leverage dan Likuiditas Terhadap Kualitas Laba pada Perusahaan Sektor Makanan dan Minuman yang Terdaftar di BEI Periode 2020-2023 Insania, Nichlata; Widayawati, Eny; Ikaningtyas, Maharani; Trimono, Trimono
Jurnal Bisnis Indonesia Vol 16, No 2: Oktober 2025
Publisher : Program Studi Ilmu Administrasi Bisnis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jbi.v15i2.5424

Abstract

Penelitian ini bertujuan untuk menguji pengaruh Leverage yang diproksikan oleh   Debt to Equity (DER), dan Likuiditas yang diproksikan Current Ratio (CR) terhadap Kualitas laba pada Perusahaan Makanan dan Minuman yang Terdaftar di Bursa Efek Indonesia (BEI) periode 2020-2023. Populasi yang digunakan dalam penelitian ini adalah 84 perusahaan dan sampelnya adalah 16 perusahaan dengan menggunakan Teknik Purposive Sampling. Analisis yang digunakan dalam penelitian ini Analisis Regresi Linier Berganda menggunakan SPSS. Hasil penelitian ini menyatakan secara parsial  Leverage yang di ukur dengan Debt to Equity (DER) dan Likuiditas yang di ukur Current Ratio (CR) berpengaruh signifikan terhadap Kualitas laba. Hasil penelitian secara simultan menunjukkan bahwa Leverage yang di ukur Debt to Equity (DER), dan  Likuiditas yang di ukur Current Ratio (CR) berpengaruh  terhadap Kualitas Laba sedangkan kontribusi yang diberikan oleh Leverage yang di ukur  Debt to Equity (DER), dan  Likuiditas yang di ukur Current Ratio (CR) terhadap Kualitas Laba sebesar 97,6 %, sedangkan sisanya sebesar 2,4% di jelaskan oleh variabel lain yang tidak termasuk dalam penelitian ini
Co-Authors Abda Abda Abdullah Abdullah Adam, Cindi Adelia Adelia, Adelia Adiwidyatma, Afdhal Reshanda Afidria, Zulfa Febi Amanillah, Rahmatul Amri Muhaimin Andreas Nugroho Sihananto Ardiani, Ardia Eva Arif, Farah Yusnaida Arifta, Septia Dini Arrum Marwani Aurelia, Cenditya Ayu Aviolla Terza Damaliana Aviolla Terza Damaliana Aviolla Terza Damaliana Awang, Wan Suryani Wan Azni Aisyah Azzahra, Adelia Ramadhina Bagus Widduro Bainar Bainar, Bainar Bey Lirna, Cagiva Chaedar Carissa, Savvy Prissy Amellia Cindi Adam Damaliana, Aviolla Terza Desy Miftachul Ilmi Arifin Putri Dewi, Ni Luh Ayu Nariswari Di Asih I Maruddani Di Asih I Maruddani Di Asih I Maruddani Diash, Hakam Dzakwan Dinda Putri Arnindi Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Edi Sugiyanto Eny Widayawati Erna Novita Anggie Fahrudin, Tresna Maulana Fairuz Luthfia Winoto Putri, Maretta Farkhan Febri Giantara Febriyanti, Alvi Yuana Febyanti, Iin Hadi, Surjo Hadiyan Pradipta, Alvino Hasan Hendri Prabowo Herlina Herlina Hervrizal, Hervrizal I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa I Gusti Putu Asto Buditjahjanto idhom, Mohammad Ikaningtyas, Maharani Ikaningtyas, Maharani Ilil Musyarof Asfiani Imanta Ginting Imelda Widya Ningrum Indira Zein Rizqin Insania, Nichlata Irawan, Tanaya Anindita Irma Amanda Putri Jacinda Ardina Gestyaki Kartika Maulida Hindrayani Kassim, Anuar bin Mohamed Khairunisa, Adenda Khosyi, Hanun Aufa Nur Kusdani, Kusdani Kuswardana, Dendy Arizki Linggasari, Dienna Eries Lisanthoni, Angela M Zufar Irhab S Putra Maharani Ikaningtyas Maruddani, Di Asih Mas'ad Mas'ad Maulana Pasha, Naufal Ricko Maulidiyyah, Nova Auliyatul Milla Akbarany Baktiar Putri Mochammad Abudrrochman Faiz Mohammad Idhom Mohammad Idhom Mohammad Idhom Muhaimin, Amri Muhammad Muharrom Al Haromainy Muhammad Nasrudin Muhammad Nasrudin Munoto Nabila, Nasywa Azzah Nabilah Selayanti Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nasution, Baktiar Nathania, Vannesa Nevia Desinta Putri Ningrum, Imelda Widya Nova Auliyatul Maulidiyyah Novita Anggraini Nugraheni, Setiawati Oktaviani, Sheny Eka Panglima, Talitha Fujisai Prisma Hardi Aji Riyantoko Prismahardi Aji Riyantoko Putri, Irma Amanda Rafiqah, Lailan Rafli Feandika Nugroho, Muhammad Renaldi, Sahat Rhomaningtias, Lina Riswanda, Mohammad Nizar Ryan Dana, Alvin Sabela, Sefilah Naurah Safira Devi, Arsita Safira, Alya Mirza Salma Namira, Alivia Sekar Arum Melati Selly Rizkiyah Shindi Shella May Wara Sonhaji, Abdulah Sugiarti, Nova Putri Dwi Suprapto, Rheinka Elyana Susrama Mas Diyasa , I Gede Syamsul Rizal Tarno Tarno Taufik, Ikbar Athallah Terza Damaliana, Aviolla Tiara Audrey Anugerah Hadin Tresna Maulana Fahrudin Utami, Rianti Siswi Utriweni Mukhaiyar Valentina, Tiara Wahyu Syaifullah Jauharis Saputra Wardah, Salsabila Wibowo, Muhammad Bagas Satrio Widayawati, Eny Widison, Daffin Tanjiro Yuciana Wilandari Zalfa Assyadida, Azizah