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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer LEGITIMASI: Jurnal Hukum Pidana dan Politik Hukum Law and Justice Jurnal Ilmiah Ekonomi Islam Jurnal Ekonomi & Keuangan Islam SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL INFOKUM Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Aafiyah Health Research (JAHR) Jurnal Multidisipliner Bharasa Media Journal of General Computer Science (MJGCS)
Sukoco, Bambang
Universitas Muhammadiyah Surakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Beyond access: Islamic financial literacy and women’s empowerment Sukoco, Bambang; Utami, Cahyaning Budi; Ivantri, Madha Adi; Awdalkrem, Alhussain
Jurnal Ekonomi & Keuangan Islam Volume 12 No. 1, January 2026
Publisher : Faculty of Economics, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/JEKI.vol12.iss1.art10

Abstract

Purpose – This study examines the effect of Islamic financial literacy on women’s empowerment using Islamic financial inclusion as a mediating variable. In addition, digital financial literacy was examined to capture its complementary role in expanding women’s financial participation.Methodology – Using data of 140 female who were or had been married, this group reflects household decision-making roles and provides valuable insights into women’s empowerment. The relationships among the variables were analyzed using structural equation modeling-partial least squares (SEM-PLS). Findings – The results show that both Islamic financial literacy and digital financial literacy significantly enhance Islamic financial inclusion and women’s empowerment. However, Islamic financial inclusion does not significantly mediate the relationship between literacy (Islamic and digital) and women’s empowerment. Implications – The findings emphasize the need to strengthen financial literacy programs, both digital and Islamic, as part of broader efforts to advance women's empowerment in OIC (Organization of Islamic Cooperation) member countries. Financial institutions and policymakers should integrate literacy initiatives with inclusion strategies to ensure that women fully benefit from Sharia-compliant financial services.Originality – This study provides new evidence linking Islamic financial literacy, digital financial literacy, and Islamic financial inclusion to explain women’s empowerment. This offers insights into the pathways through which literacy and inclusion interact, particularly in the context of Islamic finance.
THE EFFECTIVENESS OF LAW ENFORCEMENT AGAINST ONLINE GAMBLING CRIMES IN INDONESIA: A CRIMINAL LAW AND CRIMINOLOGICAL PERSPECTIVE Kuswardani, Kuswardani; Sukoco, Bambang
SOSIOEDUKASI Vol 15 No 2 (2026): SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL
Publisher : Fakultas Keguruan Dan Ilmu Pendidikan Universaitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/sosioedukasi.v15i2.7814

Abstract

This study aims to analyze the effectiveness of law enforcement against online gambling crimes in Indonesia from criminal law and criminological perspectives. The rapid development of information technology has transformed conventional gambling into more complex, anonymous, and transnational digital forms, posing challenges in offender identification and regulatory adaptation. This research employs a normative juridical approach using secondary data from laws, legal doctrines, and academic literature, analyzed qualitatively through a descriptive-analytical method. The findings reveal that law enforcement remains suboptimal despite existing regulations such as the Information and Electronic Transactions Law. Key obstacles include limited institutional capacity, increasingly sophisticated modus operandi, and the transnational nature of the crime. From a criminological perspective, this phenomenon is driven by opportunity structures, ease of technological access, and economic motivations. This study highlights the need for regulatory strengthening, institutional capacity improvement, and integrated legal and criminological approaches.
Fitur Information Gain untuk Meningkatkan Nilai Performa Pengklasifikasi Machine Learning pada Analisis Sentimen Komentar Spam Pengguna Youtube Jasmir, Jasmir; Gunardi, Gunardi; Rohaini, Eni; Naibaho, Ronald; Sukoco, Bambang; Jasmir , Jasmir
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 2: April 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.132

Abstract

Perkembangan pesat media sosial telah memberikan ruang bagi setiap individu untuk menyampaikan pendapat, baik berupa komentar positif maupun negatif terhadap konten yang mereka akses. Kemudahan dalam memberikan opini secara daring ini berdampak pada semakin besarnya jumlah ulasan yang tersedia. Namun, volume ulasan yang sangat besar sering kali sulit untuk dianalisis secara manual dan berpotensi menimbulkan bias dalam penilaian. Untuk mengatasi permasalahan tersebut, diperlukan pendekatan otomatis melalui klasifikasi sentimen yang bertujuan mengelompokkan opini pengguna ke dalam kategori positif atau negatif. Dalam penelitian ini digunakan tiga algoritma pembelajaran mesin, yaitu Naïve Bayes (NB), K-Nearest Neighbor (KNN), dan Random Forest (RF). Data penelitian diperoleh dari public dataset UCI Machine Learning. Fokus penelitian adalah meningkatkan kinerja klasifikasi dengan memanfaatkan teknik seleksi fitur information gain. Hasil eksperimen menunjukkan bahwa penerapan information gain secara konsisten meningkatkan performa semua algoritma yang diuji, baik pada metrik akurasi, presisi, recall, maupun f1-score. Naïve Bayes awalnya memperoleh akurasi tertinggi sebesar 74,33% pada kondisi tanpa fitur tambahan. Namun, setelah penerapan information gain, algoritma KNN menunjukkan hasil paling optimal dengan akurasi mencapai 81,28% serta performa yang relatif seimbang pada semua metrik evaluasi. Sementara itu, Random Forest juga mengalami peningkatan, meskipun tidak melampaui KNN. Secara keseluruhan, penelitian ini menegaskan bahwa pemilihan fitur yang relevan melalui information gain mampu meningkatkan efisiensi dan efektivitas klasifikasi sentimen, serta dapat menjadi pendekatan yang potensial untuk menganalisis opini dalam skala besar.   Abstract The rapid growth of social media has provided individuals with the opportunity to freely express their opinions, whether positive or negative, toward the content they encounter. The increasing ease of sharing opinions online has resulted in a massive volume of user reviews. However, the large number of reviews is difficult to analyze manually and may introduce bias in interpretation. To address this issue, sentiment classification is applied to automatically categorize user opinions into positive or negative classes. In this study, three machine learning algorithms were employed: Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Random Forest (RF). The dataset was obtained from the public UCI Machine Learning repository. The main objective of this research is to improve classification performance by utilizing feature selection through the information gain method. Experimental results demonstrate that applying information gain consistently enhances the performance of all evaluated algorithms across multiple metrics, including accuracy, precision, recall, and F1-score. Without feature selection, Naïve Bayes achieved the highest accuracy of 74.33%. However, after applying information gain, KNN outperformed the other algorithms by reaching an accuracy of 81.28% and exhibited balanced results across all evaluation metrics. Random Forest also showed improvement but did not surpass the performance of KNN. Overall, these findings highlight the importance of feature selection in improving both the efficiency and effectiveness of sentiment classification. Furthermore, the use of information gain proves to be a promising approach for large-scale opinion analysis, particularly in handling the high dimensionality of textual data.
Co-Authors Ahmad Faruk Alwi, Muhammad Khidri Arini Nur Rohmah Arman Arman Arni Rizqiani Rusydi Awdalkrem, Alhussain Budi Setiawan Dedi Gunawan Diah Priyawati Fatah Yasin Al Irsyadi Gunardi Gunardi Hermawan, Ian Aji Isandra, Nariza Islam, Syful Jasmir , Jasmir Jasmir, Jasmir Kuswardani Kuswardani, Kuswardani Madha Adi Ivantri, Madha Adi Muhammad Yasin Munawar, Zaid NAIBAHO, RONALD Nurhaedar Jafar Pramono, Kamiran Pratama, Raka Jumersyah Putra, Hengkky Restu Qoway, Octiana Atika Rohaini, Eni Sitti Patimah Utami, Cahyaning Budi Yusuf Sulistyo Nugroho