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All Journal Jurnal Gaussian Jurnal Informatika Mandalika Mathematics and Educations Journal Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Pengabdian Masyarakat Asia JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Nusantara Hasana Journal Jurnal Pengabdian Masyarakat Bangsa Journal of Community Service in Science and Engineering RENATA Jurnal Pengabdian Masyarakat Kita Semua TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS) IJTIMA': Jurnal Pengabdian Masyarakat Indonesian Actuarial Journal
Rivai, Muklas
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Analisis Peramalan Harga Saham Menggunakan Temporal Convolutional Network: Studi Kasus PT Lippo General Insurance Tbk Muklas Rivai; Ongky Setya Nugraha
Jambura Journal of Probability and Statistics Vol 6, No 2 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i2.26817

Abstract

The stock market has an important role in the Indonesian economy, but share price fluctuations are often difficult to predict accurately. The machine learning algorithm for forecasting stock price movement trends uses a Temporal Convolutional Network (TCN). This method uses a more comprehensive dataset and advanced analysis techniques to capture non-linear and dynamic patterns in stock price data. This research aims to predict the share price of PT Lippo General Insurance Tbk using Temporal Convolutional Network (TCN) to provide a more accurate and reliable forecasting model. The research method uses a quantitative approach with daily historical stock data from 2011 to 2023 which is processed through several stages, including data collection, pre-processing, model development, and performance evaluation.  The results of the study show that the stock price forecasting of PT Lippo General Insurance Tbk using the Temporal Convolutional Network (TCN) method produces values that are relatively close to the actual ones with MSE, RMSE, MAE, and MAPE indicators, respectively, being 11,076.8214; 105.2464; 63.5915; and 2.2369\%. This indicates that the TCN model is able to capture complex temporal patterns in the stock price data of PT Lippo General Insurance Tbk. The forecasting results that have been projected for the next 60 days, that the stock price of PT Lippo General Insurance for the next 60 days will tend to decrease from August 31 to November 23.  
Pemanfaatan Looker Studio untuk Mengembangkan Kompetensi Analisis dan Visualisasi Data Siswa SMKS Nurul Huda Pringsewu Rosni Rosni; Nora Madonna; Andi Fitriawati; Dani Al Mahkya; Agus Irawan; Erica Grace Simanjuntak; Ma’rufah Hayati; Nasrullah Nasrullah; Miftahul Irfan; Dwi Mahrani; Ayu Sofia; Tiara Yulita; Muklas Rivai
Jurnal Pengabdian Masyarakat Bangsa Vol. 4 No. 1 (2026): Maret
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v4i1.4194

Abstract

Kemampuan dalam memanfaatkan teknologi, khususnya dalam eksplorasi dan visualisasi data, menjadi tantangan signifikan dalam dunia pendidikan di era digital saat ini. SMKS Nurul Huda Pringsewu sebagai mitra kegiatan pengabdian menunjukkan adanya keterbatasan kompetensi siswa dalam mengolah data dan menyajikannya dalam bentuk visual yang informatif. Menanggapi permasalahan tersebut, tim Pengabdian kepada Masyarakat (PkM) melaksanakan pelatihan menggunakan aplikasi Looker Studio untuk eksplorasi data dan pembuatan dashboard statistik. Looker Studio merupakan platform berbasis web yang memudahkan pengguna dalam mengolah data numerik dan menyajikannya secara visual, sehingga proses pembacaan data tidak lagi dilakukan secara manual. Pelatihan ini bertujuan untuk meningkatkan kemampuan siswa dalam mengolah dan memvisualisasikan data menggunakan teknologi terkini. Hasil dari kegiatan ini menunjukkan peningkatan kompetensi siswa dalam mengeksplorasi data statistik serta peningkatan kualitas pembuatan dashboard yang lebih informatif dan menarik. Kegiatan ini diharapkan dapat menjadi langkah awal dalam membekali siswa dengan keterampilan digital yang relevan dengan kebutuhan industri saat ini.
PERBANDINGAN PREDIKSI INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN METODE REGRESI RANDOM FOREST DAN EXTREME GRADIENT BOOSTING Agnes Monika Simalango; Muklas Rivai; Fuji Lestari
Jurnal Gaussian Vol 15, No 2 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.2.356-367

Abstract

Indeks Harga Saham Gabungan (IHSG) serves as the benchmark of the Indonesian stock market and is characterized by nonlinear and volatile movements driven by macroeconomic conditions, trading activities, and market sentiment. This study aimed to compare the forecasting performance of Random Forest and Extreme Gradient Boosting (XGBoost) for the IHSG using historical data from January 2019 to July 2025 with an 80:20 chronological train-test split. Model performance was evaluated using RMSE and MAPE, followed by variable importance analysis and Value at Risk (VaR) estimation to asses potential investment losses. The results showed that Random Forest outperformed XGBoost, achieving an RMSE of 117.54 and a MAPE of 0.90%, whereas XGBoost produced an RMSE of 136.45 and a MAPE of 1.03%. Variable importance analysis identified the highest index value as the most influential predictor, followed by the lowest index value, the opening index value, and trading volume. Furthermore, the estimated expected return was 0.00014, with VaR values of IDR 470,000.00, IDR 610,000.00, and IDR 960,000.00 at the 90%, 95%, and 99% confidence levels, respectively. Based on the evaluation results, Random Forest achieved better forecasting accuracy than XGBoost for the IHSG.
Pelatihan Olah Data dan Visualisasi Data Statistik dalam Peningkatan Kompetensi Perangkat Desa Badran Sari, Lampung Selatan Ayu Sofia; Fuji Lestari; Muklas Rivai; indah Gumala andirasdini; Dila Tirta Julianty
TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Vol 5 No 1 (2025): TEKNOKREATIF : Jurnal Pengabdian kepada Masyarakat Volume 5 No 1
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LP2M), Institut Teknologi Sumatera, Lampung, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/teknokreatif.v5i1.1873

Abstract

The use of data processing skills is something that is very important in various fields. The data processing process can use various applications, one of which is the number/data processing application which is commonly known as the Ms. Excel application. Ms. Excel is a software program that allows users to process and calculate numerical data so that calculations and reading data are no longer done manually. The problem with partners is the lack of competence of village officials regarding the use of technology in processing village data. Based on the problems faced by partners, the PkM team offers a solution, namely providing training in processing and analyzing statistical data using Ms. Excel which aims to help village officials to be able to process data and be able to visualize the data into images/graphs that are more attractive to the community so that able to improve the quality of data processing contained in village officials.
Pelatihan Pengolahan dan Analisa Data Statistik Untuk Meningkatkan Kompetensi Guru SMPN di Kalianda, Lampung Selatan Indah Gumala Andirasdini; Ayu Sofia; Fuji Lestari; Amalia Listiani; Tiara Yulita; Dila Tirta Julianty; Muklas Rivai
TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Vol 4 No 2 (2024): TEKNOKREATIF : Jurnal Pengabdian kepada Masyarakat Volume 4 No 2
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LP2M), Institut Teknologi Sumatera, Lampung, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/teknokreatif.v4i2.1875

Abstract

Salah satu wujud pengembangan diri seorang guru adalah dengan melakukan dan menulis laporan penelitian. Penelitian dan penulisan laporan hasil penelitian terkait kinerja pembelajaran seorang guru merupakan salah satu upaya evaluasi (refleksi) terhadap kinerja seorang guru di dalam kelas. Evaluasi ini dilakukan dengan melaksanakan Penelitian Tindakan Kelas (PTK). Permasalahan yang terjadi pada PTK adalah kurangnya kompetensi guru dalam melakukan pengolahan data, menggunakan tools, dan menerapkan model-model statistika yang cocok untuk kasus-kasus yang terjadi di kelas. Pada jurnal ini membahas peningkatan kompetensi guru-guru SMPN melalui pelatihan olah data dan analisis data statistik menggunakan JASP. Tingkat pemahaman dan kompetensi guru diukur menggunakan kuesioner yang diberikan pada sebelum dan setelah melakukan pelatihan. Pelatihan ini memberikan perubahan tingkat pemahaman dan kompetensi yang signifikan berdasarkan bidang kompetensi yang ditanyakan. Hal ini dapat dilihat dari gap responden yang diperoleh saat sebelum pelatihan dan setelah pelatihan dilakukan.
Analysis of the Health Social Security Administration (BPJS Kesehatan) Claim Amount using Random Forest Regression Indah Gumala Andirasdini; Desta Saputra; Muklas Rivai; Septia Eka Marsha Putra
Indonesian Actuarial Journal Vol. 1 No. 1 (2025)
Publisher : Persatuan Aktuaris Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65689/iajvol01no1pp001-008

Abstract

Claims paid by hospitals need to be identified to verify the accuracy of health services, maintain service quality, and optimize services provided to the Health Social Security Administration (BPJS Kesehatan) participants. This aligns with the third goal of the Sustainable Development Goals (SDGs), which is to ensure healthy lives and promote well-being for all ages, particularly in the context of universal health coverage. The difference in tariffs set by BPJS Kesehatan (INA-CBGs) compared to the amount paid by hospitals has led to a problem that can harm health facilities, such as delayed claim payments. This study aims to analyze the amount of claims paid by a regional hospital to BPJS Kesehatan participants using machine learning with the Random Forest Regression method. Based on this modeling, it was found that the severity of patients, length of stay, and type of illness are the most significant factors in determining the amount of claims. This study has an accuracy value of 81.89%, an adjusted R-square value of 80.4%, and a Mean Absolute Percentage Error (MAPE) of 18.11% in estimating the amount of claims.
Peningkatan Literasi Keuangan Siswa melalui Pemanfaatan Tabel Amortisasi dalam Perencanaan Cicilan Pinjaman Online Lestari, Fuji; Andirasdini, Indah; Tirta Julianty, Dila; Rivai, Muklas
IJTIMA': Jurnal Pengabdian Masyarakat Vol. 3 No. 2 (2026): Oktober
Publisher : UIR Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/ijtima.2026.28358

Abstract

The development of financial technology has broadened access to financial services while simultaneously increasing the risk of consumptive financial behavior among young people. Low levels of financial literacy among students contribute to limited understanding of repayments, interest rates, loans, and debt-related risks. This activity aimed to improve the financial literacy of students at MAN 1 Bandarlampung through the implementation of the amortization concept as a basis for healthy and rational loan repayment planning. The program employed interactive lectures, case simulations, hands-on practice in developing amortization tables using Microsoft Excel, and evaluations through pre-tests and post-tests. The results demonstrated a significant improvement in students’ understanding, with average scores increasing from 46,7 to 85 after the training. In addition, the percentage of students who were able to explain the concept of amortization correctly increased from 22% to 93%. Through a practice-oriented and case-based learning approach, this program effectively enhanced students’ financial literacy and strengthened their awareness of responsible loan management. The activity also produced educational modules and digital amortization templates that can be independently utilized by students as sustainable learning tools for personal financial planning.
Co-Authors Agnes Monika Simalango Agus Irawan Aldila Nur Indah Berliana Ratam Amalia Listiani Andi Fitriawati Andi Fitriawati Andirasdini, Indah Annisa Hevita Gustina Kumalasari Saefulloh Antika, Siluh Putu Dela Anwaar Al'Fathoni, Muhammad Khoirul Aulia Khairani Hutabarat Ayu Sofia Azzanina, Nanda Baiti, Putri Isnaini Cahyaning Berliana Ratam, Aldila Nur Indah Dani Al Mahkya Desta Saputra Dila Tirta Julianty Dwi Mahrani Erica Grace Simanjuntak Erinna Fredella Fuji Lestari Fuji Lestari Gustriza Erda Indah Gumala Andirasdini Irfan, Miftahul Irwan, Sri Efrinita Karina Sylfia Dewi Lase, Marie Vivien Lidyasari, Nadia Bintang Listiani, Amalia Marisa Marisa Ma’rufah Hayati Miftahus Sholihin Misbahul Huda Nadeak, Natasha Putri Nainggolan, Lorena Ulitara Nasrullah Bachtiar Nasrullah Nasrullah Nora Madonna Ongky Setya Nugraha Putri Dina Sari Rinaldi, Fanny Dwi Putri Rohmah, Iffah Nurul Rosni, Rosni Selvira, Natalia Aisa Septia Eka Marsha Putra Situmorang, Anita Grace Pretty Syarif Abdullah Tirta Julianty, Dila Wicaksono, Agung Satrio Yulita, Tiara