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All Journal Syntax Literate: Jurnal Ilmiah Indonesia SOLUSI Brilliance: Research of Artificial Intelligence
Khoirunisa, Rifa
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Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Imputing Data and Predicting Waste with Machine Learning in East Java Khoirunisa, Rifa; Sani, Ahmad Faisal; Riatma, Darmawan Lahru; Masbahah, Masbahah; Rachman, Yusuf Fadlila
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6461

Abstract

Indonesia's waste problem continues to be a pressing environmental issue, along with the increasing population and urbanization activities. The increase in population and changes in consumption patterns have led to a significant spike in waste generation in Indonesia. Machine learning-based approaches become highly relevant in supporting accurate predictive systems to estimate waste generation, so that it can be used as a basis for policy making and planning for more effective and sustainable waste management. However,the issue of missing data is a common challenge in environmental data processing, including in the recording of waste generation. Incomplete waste generation data can hinder accurate analysis and prediction, which are essential for effective environmental management planning. This study aims to analyze the effectiveness of various data imputation methods and to develop a predictive model for waste generation in East Java Province using a machine learning approach. The imputation techniques tested include Mean Imputation, K-Nearest Neighbor (KNN), and Interpolation, while the predictive models used include Random Forest, Gradient Boosting, and KNN Regression. The dataset was obtained from the official SIPSN (National Waste Management Information System) website. Model performance was evaluated using metrics such as Root Mean Square Error (RMSE). The results indicate that the combination of KNN Imputer with the Gradient Boosting prediction model is effective in addressing missing data and predicting waste generation in East Java Province, achieving an RMSE value of 0.147. These findings are expected to support more accurate decision-making in waste management planning for the province.
Benchmarking GPU Passthrough Performance on Docker for AI Cloud System Sani, Ahmad Faisal; Khoirunisa, Rifa; Riatma, Darmawan Lahru; Rachman, Yusuf Fadlila; Masbahah, Masbahah
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6794

Abstract

The use of artificial intelligence (AI), which depends only on CPU resources, tends to result in longer execution times or CPU time. Especially when handling large amounts or complex workloads. To overcome that issue, the use of a graphics processing unit (GPU) becomes a significant support. GPUs can significantly speed up AI inferences through their parallel architecture. One recent approach to integrating GPUs into an AI system is called GPU passthrough. Either natively (native environment), or through Docker environment. However, until recently, the efficiency and results between those methods have remained unexplored, particularly in local cloud environment.This study aimed to compare GPU performance between native and Docker environment using a 10.000 x 10.000 matrix multiplication workload with the TensorFlow frameworks. Execution time and GPU performance measured using the nvidia-smi tool. Data is recorded automatically in CSV format. The researcher used the NVIDIA CUDA environment to ensure full compatibility with GPU acceleration.The result demonstrated that GPU processing in native environment had faster average time, as in 1.52 seconds. In another case, GPU passthrough in docker environment demonstrated higher GPU utilization, as in 86.2% but had a longer execution time.These findings indicate that GPU overhead occurred in docker environment due to the containerization layer. On the contrary, the native environment resulted in shorter execution time, even though it did not maximize the GPU utilization. These results provide valuable basis data for technical decision-making in GPU-based AI deployment in a limited environment.
Literature Review: Virtual Reality Sebagai Media Imersif Dalam Terapi Kognitif Untuk Penanganan Stress dan Depresi Khoirunisa, Rifa; Sani, Ahmad Faisal; Masbahah, Masbahah; Rachman, Yusuf Fadlila; Haqimi, Nur Azizul
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v10i10.62104

Abstract

Stres dan depresi merupakan masalah kesehatan mental yang semakin meningkat secara global, terutama pada kelompok usia produktif, dan berdampak signifikan terhadap kualitas hidup individu. Penelitian ini bertujuan untuk meninjau secara sistematis penerapan Virtual Reality (VR) sebagai media imersif dalam terapi kognitif untuk penanganan stres dan depresi. Terapi kognitif, meskipun telah terbukti efektif dalam mengurangi gejala maladaptif, seringkali menghadapi kendala dalam hal aksesibilitas, motivasi pasien, dan tingkat keterlibatan dalam proses terapi. VR muncul sebagai inovasi yang menawarkan pengalaman interaktif dan lingkungan aman yang dapat meningkatkan efektivitas terapi. Metode penelitian menggunakan Systematic Literature Review (SLR) terhadap 118 artikel terindeks Scopus dengan rentang publikasi 2022–2025. Hasil analisis menunjukkan bahwa 10 artikel mendukung efektivitas VR dalam menurunkan gejala stres dan depresi, sedangkan 8 artikel menekankan pengalaman subjektif pasien yang cenderung positif. Keunggulan utama VR adalah sifatnya yang imersif, fleksibilitas personalisasi, serta kemampuan meningkatkan keterlibatan pasien. Namun, tantangan yang ditemukan meliputi keterbatasan teknologi, biaya implementasi, dan isu kenyamanan pengguna. Faktor penentu keberhasilan penerapan meliputi kualitas perangkat, kesiapan tenaga profesional, serta motivasi dan karakteristik pasien. Implikasi penelitian ini menunjukkan bahwa VR berpotensi besar menjadi media pendukung terapi kognitif yang menjanjikan dalam praktik klinis, meskipun penelitian lebih lanjut diperlukan untuk mengkaji dampak jangka panjang dan pengalaman pengguna secara lebih mendalam.
PENGARUH RASIO KEUANGAN TERHADAP PROFITABILITAS PADA BANK BUMN DAN SWASTA PERIODE TAHUN 2018 – 2022 Khoirunisa, Rifa; Amalia, Naini Rizka
Solusi Vol. 22 No. 1 (2024): January
Publisher : Fakultas Ekonomi, Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/slsi.v22i1.8364

Abstract

Secara universal profitabilitas maupun yang biasa diujarkan dengan rentabilitas yakni kemahiran industri guna menciptakan laba sepanjang periode tertentu. Profitabilitas industri menampilkan perbandingan antara laba dengan aktiva maupun modal yang menciptakan laba tersebut. Studi ini bertujuan guna menguji pengaruh Capital Adequacy Ratio (CAR), Loan to Deposit Ratio , serta Non Performance Loan (NPL) terhadap profitabilitas industri perbankan. Ilustrasi riset ini terdiri atas 6 industri perbankan yang diseleksi secara purposive sampling dari populasi riset ialah industri perbankan yang terdaftar di Bursa Dampak Indonesia (BEI) dari tahun 2018 hingga tahun 2022. Perlengkapan uji statistik berbentuk SPSS 25 yang digunakan untuk dapat melaksanakan uji asumsi klasik serta analisis regresi berganda. Hasil riset ini menampilkan model dari regresi pada riset ini sudah penuhi uji asumsi klasik secara totalitas, yang berarti jika model regresi ini sudah bebas dari indikasi multikolinieritas, heteroskedastisitas, autokorelasi, dan informasi yang dihasilkan terdistribusi normal. Analisis regresi berganda menampilkan kalau Capital Adequacy Ratio (CAR) tidak berpengaruh signifikan terhadap profitabilitas, Loan to Deposit Ratio (LDR) tidak berpengaruh signifikan terhadap profitabilitas dan Non Performance Loan (NPL) berpengaruh signifikan terhadap profitabilitas
Co-Authors Amalia, Naini Rizka Haqimi, Nur Azizul Masbahah Masbahah, Masbahah Rachman, Yusuf Fadlila Riatma, Darmawan Lahru Sani, Ahmad Faisal