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All Journal Indonesian Journal of Multidiciplinary Research (IJoMR) Jurnal Teknologi Lingkungan Lahan Basah Malcom: Indonesian Journal of Machine Learning and Computer Science
Hidayat, Muhammad Alif
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Energy Engineering Environmental Science Immunology & microbiology Public Health Social Sciences Other

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The Journey of A Math: As a Mathematics Learning Innovation Husnah, Annisa Ul; Hidayat, Muhammad Alif; Jannah, Miftahul
Indonesian Journal of Multidiciplinary Research Vol 1, No 1 (2021): IJOMR: VOLUME 1, ISSUE 1, 2021
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1525.503 KB) | DOI: 10.17509/ijomr.v1i1.33814

Abstract

In the era of the Industrial Revolution 4.0, science and technology played an important role in the future. Therefore, education is fundamental in the formation and development of human resources. Thus, they can contribute to the development of science and technology, especially in Mathematics. Mathematics has an important role in the future, but as is well-known Mathematics is one of the subjects that is considered difficult. Many students complain about this lesson for several reasons, such as the monotonous learning and the way the teacher does not vary. Thus, it is difficult to understand. To solve this problem, there is an innovation called TJAM. TJAM is an application that is devoted to learning Mathematics. The features of TJAM are as follows: MyMaths, Math Stories, FunMath, and MathUrgent features. The research method we use is qualitative. The purpose of TJAM is to make students like mathematics and not be afraid of mathematics because in TJAM students can find features that will help them in learning Mathematics. TJAM has an advantage when compared to similar applications, TJAM has the FunMath and MathStorie features where these two features will make it easy for students to learn mathematics. Through TJAM, students are expected to like math and be helped by the features provided.
Analisis Peningkatan Efisiensi Energi Pada Sistem Distribusi Air Minum Area Pelayanan Kalidoni Perumda Tirta Musi Kota Palembang Hidayat, Muhammad Alif; Slamet, Agus
Jurnal Teknologi Lingkungan Lahan Basah Vol 12, No 1 (2024): Januari 2024
Publisher : Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jtllb.v12i1.73553

Abstract

Perumda Tirta Musi menggunakan pompa untuk distribusi air ke seluruh pelanggannya. Konsumsi energi tertinggi Perumda tirta musi pada tahun 2022 pada urutan ketiga adalah pada Instalasi booster yang menggunakan 19% dari konsumsi energi listrik. Konsumsi energi tertinggi Perumda tirta musi pada tahun 2022 pada urutan ketiga adalah pada Instalasi booster yang menggunakan 19% dari konsumsi energi listrik. Pelaksanaan Audit energi dapat memperlihatkan kualitas daya yang digunakan oleh suatu sistem. pelaksanaan audit energi pada pompa dapat memperlihatkan efisiensi pompa yang sedang digunakan sehingga dapat dilaksanakan evaluasi. Salah satu cara untuk dapat melaksanakan penghematan energi adalah dengan cara mengatur kecepatan putaran pompa. pada pompa yang sudah dipasang Variable Speed Drive, pompa dapat diatur kecepatannya dan hal ini dapat mengurangi beban energi yang digunakan. Hasil pengukuran yang telah dilaksanakan efisiensi pompa Booster Kalidoni, Pompa 1 58,55%, Pompa 2 57,54%, dan Pompa 3 62,43%, dengan rekomendasi Pompa 1 dan 2 adalah penyetelan kembali impeller dan pembersihan sementara untuk pompa 3 masih dalam kondisi baik dan tidak memerlukan perbaikan. Peluang peningkatan efisiensi energi yang dapat dilakukan antara lain adalah penjadwalan pompa pada booster kalidoni dengan melaksanakan program tersebut bisa mengurangi kebutuhan energi sebesar 8,71% atau 17.635,69 kWh.
Tourism Marketing Intelligence: Machine Learning for Demand Forecasting Using Google Trends in Indonesia Ghofar, Khidhir Akbar; Hidayat, Muhammad Alif; Adib, Adib
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2628

Abstract

Tourism demand forecasting remains challenging due to the dynamic nature of consumer behavior and the lag in traditional economic indicators. This study investigates the predictive power of online search behavior captured through Google Trends in forecasting international tourist arrivals to Indonesia, integrating digital footprints with macroeconomic indicators using machine learning approaches. Monthly data spanning January 2011 to December 2024 (N=168) were collected from multiple sources: tourist arrivals from Statistics Indonesia, Google Trends composite index from 11 destination-related keywords, exchange rates, and consumer price index. Three machine learning algorithms Random Forest, XGBoost, and LSTM were compared using temporal train-test split (80:20). Random Forest demonstrated superior performance with Test MAPE of 14.12% and R² of 0.811, outperforming XGBoost (MAPE 21.41%) and LSTM (MAPE 24.56%). Feature importance analysis revealed that Google Trends emerged as the second most important predictor (12.22% contribution), surpassing all economic indicators combined (7.20%), validating its role as a leading indicator of tourism demand. This study contributes methodologically by providing empirical evidence for the value of integrating digital behavioral signals with traditional predictors in emerging destination contexts, and practically by demonstrating deployment-ready forecasting accuracy for tourism stakeholders in Indonesia.
Co-Authors Adib Adib, Adib Agus Slamet Husnah, Annisa Ul Khidhir Akbar Ghofar Miftahul Jannah