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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 JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) 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 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 International Journal of Community Service International Journal of Data Science, Engineering, and Analytics (IJDASEA) Al Khidma: Jurnal Pengabdian Masyarakat 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 STATISTIKA Kohesi: Jurnal Sains dan Teknologi 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
Trimono, Trimono
Data Science Department, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
EMPLOYEE VOLUNTARY ATTRITION PREDICTION AT PT.XYZ: ENSEMBLE MACHINE LEARNING APPROACH WITH SOFT VOTING CLASSIFIER Bey Lirna, Cagiva Chaedar; Trimono, Trimono; Damaliana, Aviolla Terza
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2007

Abstract

This research addresses the complexity of employee attrition challenges at PT.XYZ. The main objective is to develop a predictive system for potential voluntary employee attrition by focusing on an in-depth analysis of the factors contributing to attrition at PT.XYZ. The research utilizes data containing information on the job history of PT.XYZ employees from 2018 to 2023. The method employed in the research is a soft voting ensemble classifier model, incorporating SVM, decision tree, and logistic regression, supported by relevant literature. Analysis and exploration of historical data of PT.XYZ employees are conducted to identify key factors influencing employees' decisions to leave the company. Careful data preprocessing is carried out to ensure dataset quality before applying it to the soft voting classifier model. The results of the soft voting classifier modeling used in this research achieve excellent accuracy in both training and testing datasets with respective accuracy percentages of 99% and 98%. Based on the final results of applying the soft voting classifier model, it is expected to provide deep insights and solutions to enhance employee retention at PT.XYZ.
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.
Co-Authors Abda Abda Abdullah Abdullah Adam, Cindi Ade Irma Agustian Adiwidyatma, Afdhal Reshanda Aliya Dasa Pramesthi Amanillah, Rahmatul Amri Muhaimin Andreas Nugroho Sihananto Ardiani, Ardia Eva Arif, Farah Yusnaida Arifta, Septia Dini Aurelia, Cenditya Ayu Aviolla Terza Damaliana 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 Di Asih I Maruddani Diash, Hakam Dzakwan Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Dwi Arman Prasetya Edi Sugiyanto Fahrudin, Tresna Maulana Fairuz Luthfia Winoto Putri, Maretta Faiz, Mochammad Abudrrochman Farkhan Febri Giantara Febriyanti, Alvi Yuana Febyanti, Iin Fitriani, Aulia Nur 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 Icha Rohmatul Jannah idhom, Mohammad Ikaningtyas, Maharani Ikaningtyas, Maharani Imelda Widya Ningrum Indira Zein Rizqin Insania, Nichlata Irawan, Tanaya Anindita Irma Amanda Putri Kartika Maulida Hindrayani Kartini Kartini Kassim, Anuar bin Mohamed Khairunisa, Adenda Khosyi, Hanun Aufa Nur Kusdani, Kusdani Kuswardana, Dendy Arizki Linggasari, Dienna Eries Lisanthoni, Angela Maharani Ikaningtyas Maruddani, Di Asih Marwani, Arrum Mas'ad Mas'ad Maulana Pasha, Naufal Ricko Maulidiyyah, Nova Auliyatul Milla Akbarany Baktiar Putri Mohammad Idhom Mohammad Idhom Muhaimin, Amri Muhammad Muharrom Al Haromainy Muhammad Nasrudin Munoto Nabila, Nasywa Azzah Nabilah Selayanti 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 Prismahardi Aji Riyantoko Putra, Andrawana Putri Ariyani, Kinanthi Putri, Irma Amanda Putri, Nabila Rizky Amalia Putri, Nevia Desinta 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 Selly Rizkiyah 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 Tresna Maulana Fahrudin Utami, Rianti Siswi Utriweni Mukhaiyar Valentina, Tiara Wardah Ariij Adibah Wardah, Salsabila Wibowo, Muhammad Bagas Satrio Widayawati, Eny Widayawati, Eny Widduro, Bagus Widison, Daffin Tanjiro Yuciana Wilandari yuliza, eva Zalfa Assyadida, Azizah