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All Journal Jambura Journal of Mathematics bit-Tech KUBIK: Jurnal Publikasi Ilmiah Matematika JUTECH : Journal Education and Technology Jurnal Pengabdian Masyarakat ALMURTADO: Journal of Social Innovation and Community Service Jurnal Komtika (Komputasi dan Informatika)
Rahman, Alrafiful
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Pengenalan Standar Akuntansi di Indonesia bagi Siswa SMK Al-Hikmah Jakarta Selatan Jalaludin, Paiz; Romantica, Krishna Prafidya; Amigo, Royyan; Nuraini, Ani; Rahman, Alrafiful
Bisma: Jurnal Pengabdian Masyarakat Vol. 2 No. 1 (2024): Jurnal Pengabdian Masyarakat (Bisma)
Publisher : Universitas Darunnajah, Jakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61159/bisma.v2i1.267

Abstract

Kredibilitas dan keandalan laporan keuangan merupakan salah satu hal yang sangat penting karena berkaitan langsung dengan proses pengambilan keputusan. Untuk memastikan hal tersebut, laporan keuangan harus disusun sesuai dengan standar yang berlaku, agar terdapat konsistensi, relevansi, dan keseragaman sehingga dapat dibandingkan dengan laporan keuangan perusahaan lain. Oleh karena itu, diperlukan Standar Akuntansi Keuangan yang formal dan legal. Tulisan ini berisi laporan kegiatan pengabdian masyarakat yang bertema pengenalan standar akuntansi keuangan di Indonesia, yang dilaksanakan di SMK Al-Hikmah. Kegiatan ini bertujuan untuk memberikan wawasan tambahan kepada siswa tentang perkembangan standar akuntansi di Indonesia. Selain itu, kegiatan ini juga merupakan salah satu bentuk Tridharma Perguruan Tinggi yang dilaksanakan oleh Tim Pengabdi Program Studi Sains Aktuaria Universitas Darunnajah, Jakarta. Para peserta yang mengikuti kegiatan ini sangat antusias dan penuh semangat karena materi yang disampaikan relevan dengan jurusan yang mereka pelajari. Tingkat kesetujuan dan kepuasan peserta pada kegiatan pengabdian ini sangat tinggi, yaitu mencapai 87,60%.
Implementasi Metode Fuzzy Black-Scholes Real Options Valuation pada Rencana Investasi Smelter Nikel Jalaludin, Paiz; Rahman, Alrafiful; Andirasdini, Indah Gumala
KUBIK Vol 8, No 2 (2023): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v8i2.29738

Abstract

Indonesia is the largest nickel producing country in the world in 2022 by contributing 48.48% of the world's total nickel production. Therefore, the Indonesian government pays attention to the development of smelter companies by planning to build around 53 companies in 2024, and 56.60 percent of them are nickel smelters. The potential of the nickel smelter needs attention from various circles, especially academics from various disciplines. One that needs attention is the study of the method of evaluating the economic value of the investment plan in the nickel smelter company. The DCF method, although practical and widely used, still has a drawback, which is that it does not pay attention to the flexibility of managers' decision-making in the middle of the ongoing investment period. As a solution, the real options valuation (ROV) method provides flexibility features in making these decisions. Among the real options methods that are often used is the Black-Scholes formula which is considered the most rigid but more practical real options method. However, this problem can be overcome by implementing the fuzzy number method into the ROV method, making it more flexible. The results of this study show that the fuzzy Black-Scholes ROV method is a practical method, can calculate the risks and projects flexibility, and become a solution when initial information is less available about the characteristics of nickel smelter investment projects.
Using Real Options and Geometric Brownian Motion Methods to Evaluate Petroleum Projects in Indonesia Jalaludin, Paiz; Nuraini, Ani; Rahman, Alrafiful
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26718

Abstract

There are several methods for evaluating the value of a project. The most commonly used method is the Discounted Cash Flows (DCF) method which is more practical in its use. However, the DCF method still has several weaknesses, including not paying attention to the flexibility of the manager's decision-making when the project is carried out. The Real Options method enhances this by offering more flexible and varied models. This study uses Benninga's version of the binomial method to evaluate the value of petroleum projects with the characteristics of existing companies in Indonesia. In this study, oil prices are assumed to move following the Geometric Brownian Motion (GBM) model which is commonly used in modeling the movement of a fluctuating price. In addition, the author also modifies the binomial model by including expansion options, divestment options and a combination of both. The results of this study show that the more options that managers can choose in decision-making, the greater the opportunity for the company to optimize profits and minimize losses.
PENGEMBANGAN PENGGUNAAN SISTEM UJIAN ONLINE DENGAN GOOGLE FORMS MATERI PENGETAHUAN KUANTITATIF PADA SISWA SMA 10 JAKARTA -Rahman, Alrafiful; Rahman, Alrafiful; Mahbubi, M
AlMurtado Journal of Social Innovation and Community Service Vol 1 No 01 (2024): Sinergi Ilmu dan Nilai: Pengembangan Komprehensif untuk Masyarakat Berdaya
Publisher : BAMALA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kebutuhan akan teknologi di sektor pendidikan Indonesia semakin meningkat, khususnya dalam proses pembelajaran dan evaluasi. Program pengabdian kepada masyarakat ini bertujuan untuk memperkenalkan sistem ujian online menggunakan Google Forms kepada siswa/siswi tingkat SMA sederajat. Metode yang digunakan meliputi survei kebutuhan, persiapan administratif, pelatihan pembuatan soal ujian online, dan evaluasi program. Hasil pelatihan menunjukkan bahwa peserta mampu mengaplikasikan Google Forms untuk mengumpulkan data dan melaksanakan evaluasi pembelajaran secara efektif. Keunggulan Google Forms, seperti kemudahan penggunaan, akses gratis, serta integrasi dengan Google Drive, turut mendukung efisiensi dan pengelolaan data. Sistem ini diharapkan dapat meningkatkan keterampilan siswa/siswi dalam menghadapi era digital dan mengurangi ketergantungan pada metode konvensional berbasis kertas. Pelatihan ini mendapat tanggapan positif dari peserta, dengan sebagian besar menyatakan bahwa kegiatan berjalan sangat baik dan bermanfaat.
Comparative Analysis of K-Means and Gaussian Mixture Model in Clustering Global CO2 Emissions Paramarta, Valentinus; Rahman, Alrafiful; Priska, Lely; Roken Gurning, Rizon; Ayu Purwati, Widya
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2805

Abstract

As global CO2 emissions continue to rise, identifying meaningful patterns across countries has become increasingly vital for shaping effective climate policies. However, many existing approaches rely on uniform benchmarks that overlook national emission heterogeneity. To address this gap, this study applies two unsupervised machine learning techniques K-Means and Gaussian Mixture Model (GMM) to cluster countries based on CO2 emissions from both energy and industrial sectors. The dataset consists of six key indicators, including total emissions, growth rate, and sectoral shares. After handling missing values and applying Min-Max normalization, Principal Component Analysis (PCA) was used to reduce dimensionality and aid visualization. The core objective is to compare the effectiveness of K-Means and GMM in identifying emission-based country groupings. K-Means produced three distinct clusters with strong separation, including a unique cluster dominated solely by China due to its exceptional emission profile. GMM, by contrast, generated more flexible probabilistic clusters, better capturing overlapping patterns and internal variabilities among countries. Evaluation metrics showed that K-Means outperformed GMM in silhouette score and inertia, indicating clearer boundaries, while GMM was more adept at modeling complex, non-spherical distributions. These findings reveal the trade-offs between clarity and adaptability in clustering approaches. The study demonstrates how unsupervised learning can offer actionable insights for emission-based segmentation, enabling more nuanced and differentiated mitigation strategies. By highlighting algorithm-specific strengths, this research contributes to the advancement of machine learning applications in climate informatics and supports the development of targeted international environmental responses.
IMPLEMENTASI DATA MINING DALAM PREDIKSI HARGA SAHAM BBNI DENGAN PEMODELAN MATEMATIKA MENGGUNAKAN METODE LSTM DENGAN OPTIMASI ADAM Rahman, Alrafiful; Istiyowati, Lucia Sri; Valentinus, Valentinus; Ivan, Ivan; Azis, Zainal
JUTECH : Journal Education and Technology Vol 5, No 2 (2024): JUTECH DESEMBER
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v5i2.4137

Abstract

Stock price prediction plays a crucial role in investment decision-making, allowing investors to maximize profits and minimize risks. This study implements the Long Short-Term Memory (LSTM) method with Adam optimization to predict the stock price of Bank Negara Indonesia (BBNI) based on historical stock price data from the Indonesia Stock Exchange (2001-2023). LSTM is chosen for its ability to handle sequential data and identify long-term patterns in time series. Meanwhile, the Adam optimization algorithm is used to accelerate model convergence and improve prediction accuracy. The data used includes daily stock prices (closing prices), and the research process involves data collection, preprocessing, LSTM model creation, Adam optimization, training, evaluation, and prediction. The experimental results show that the model with a batch size of 64 and 100 epochs yields an R² of 0.9928 and a MAPE of 1.53%, indicating a very high prediction accuracy. With an accuracy of 98.46%, the LSTM model with Adam optimization proves to be effective in predicting stock prices, providing excellent results for applications in investment strategies. This study demonstrates the great potential of applying data mining and machine learning techniques in more informed and data-driven stock market analysis.
Perbandingan Kinerja SVR dan XGBoost untuk Peramalan Emisi CO₂ Global berbasis Machine Learning Rahman, Alrafiful; Paramarta, Valentinus; Ida, Agnes Novita; Akbar, Mohammad Harits; Simanjuntak, Vica Sonya M
Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 1 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v9i1.13449

Abstract

The increase in global carbon dioxide (CO2) emissions has become a major concern in climate change studies. This research aims to compare the performance of two machine learning algorithms, namely Support Vector Regression (SVR) and XGBoost, in predicting global CO2 emission trends based on historical data. The dataset used includes related variables such as energy consumption, gross domestic product (GDP), and population, obtained from open data sources like Our World in Data. SVR is optimized through grid search to obtain the best parameters, while XGBoost is used as the main comparator due to its ability to handle non-linear relationships and feature interactions. Model evaluation was conducted using the MAE, RMSE, and R2 metrics. The results show that XGBoost is significantly superior with an MAE of 1745.70 and an RMSE of 2663.18, as well as an R2 value of 0.93, compared to SVR which has an MAE of 5476.54, an RMSE of 8153.37, and an R2 value of 0.82. The visualization of the prediction results also indicates that XGBoost is more accurate in following the fluctuation patterns of actual data, especially in detecting sharp changes. These findings suggest that XGBoost is a more suitable method for forecasting CO2 emissions in complex and non-linear global contexts.
Co-Authors -Rahman, Alrafiful Akbar, Mohammad Harits Amigo, Royyan Andirasdini, Indah Gumala Ani Nuraini Ayu Purwati, Widya Azis, Zainal Ida, Agnes Novita Ivan Ivan Lucia Sri Istiyowati M. Mahbubi Paiz Jalaludin Paramarta, Valentinus Priska, Lely Roken Gurning, Rizon Romantica, Krishna Prafidya Simanjuntak, Vica Sonya M Valentinus, Valentinus