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All Journal Ahkam: Jurnal Ilmu Syariah TEKNIK INFORMATIKA Jurnal Didaktik Matematika JOIN (Jurnal Online Informatika) Jurnal Ilmiah FIFO Jurnal Pilar Nusa Mandiri Journal of Applied Data Sciences Indo-MathEdu Intellectuals Journal Mimbar Agama Budaya International Journal of Social Science, Education, Communication and Economics Cendikia Pendidikan EDUKASI Journal of Current Research in Blockchain
Khairani, Dewi
Universitas Islam Negeri Syarif Hidayatullah Jakarta
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Improving Digital Safety and Ethical Awareness through Community Service Initiatives in Educational Environments Nurul Faizah Rozy; Andi Faisal Bakti; Muhammad Azhari; Dewi Khairani; Noeni Indah Sulistiya; Abraham Zakky Zulhazmi
Mimbar Agama Budaya Vol. 42 No. 1 (2025)
Publisher : Center for Research and Publication (PUSLITPEN), UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/mimbar.v42i1.47176

Abstract

This study presents the outcomes of a community service initiative conducted by Informatics Engineering students of UIN Jakarta, focusing on the enhancement of digital literacy particularly digital safety and digital ethics within the cultural context of Indonesian educational institutions. The program engaged nine schools across Jakarta, Tangerang City, South Tangerang, and Bogor, regions characterized by diverse educational and socio-cultural backgrounds. Emphasizing culturally sensitive approaches to digital interaction, the program provided participants with knowledge and ethical frameworks aligned with local values and community norms. Post-program evaluations revealed a marked increase in digital literacy, with an average score of 4.28 on a 5-point scale. Furthermore, 47.1% of participants rated the module on data security at the highest level. However, cultural barriers were also identified, including hesitancy from some institutions to adopt formal digital supervision structures, often due to traditional perceptions of trust and authority. Despite these challenges, the initiative proved effective in cultivating culturally-aware digital behavior, preparing participants to navigate the digital era while remaining rooted in local ethical standards.
Improving Indonesian Named Entity Recognition for Domain Zakat Using Conditional Random Fields Widiyanti, Nur Febriana; Sukmana, Husni Teja; Hulliyah, Khodijah; Khairani, Dewi; Oh, Lee Kyung
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.898

Abstract

In Indonesia, where the majority of the population is Muslim, one of the obligations of a Muslim is zakat. To reduce illiteracy about zakat among Muslims, they need to have access to basic information about it. In order to facilitate the acquisition of this information, this study utilized named entity recognition (NER) and defined 12 named entity classes for the zakat domain, including the pillars of Islam, various types of zakat, and zakat management institutions. The Conditional Random Fields method was used for testing Indonesian-NER in three scenarios. In the specific context of the Zakat domain, NER can extract information about organizations, individuals, and locations involved in collecting and distributing Zakat funds. This information can improve the Zakat system’s efficiency and transparency and support research and analysis on Zakat-related topics. The average performance evaluation of the Indonesian-NER model showed a precision of 0.902, recall of 0.834, and an F1-score of 0.867.
Enhancing Speech-to-Text and Translation Capabilities for Developing Arabic Learning Games: Integration of Whisper OpenAI Model and Google API Translate Khairani, Dewi; Rosyadi, Tabah; Arini, Arini; Rahmatullah, Imam Luthfi; Antoro, Fauzan Farhan
JURNAL TEKNIK INFORMATIKA Vol. 17 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i2.41240

Abstract

This study tackles language barriers in computer-mediated communication by developing an application that integrates OpenAI’s Whisper ASR model and Google Translate machine translation to enable real-time, continuous speech transcription and translation and the processing of video and audio files. The application was developed using the Experimental method, incorporating standards for testing and evaluation. The integration expanded language coverage to 133 languages and improved translation accuracy. Efficiency was enhanced through the use of greedy parameters and the Faster Whisper model. Usability evaluations, based on questionnaires, revealed that the application is efficient, effective, and user-friendly, though minor issues in user satisfaction were noted. Overall, the Speech Translate application shows potential in facilitating transcription and translation for video content, especially for language learners and individuals with disabilities. Additionally, this study introduces an Arabic learning game incorporating an Artificial Neural Network using the CNN algorithm. Focusing on the “Speaking” skill, the game applies to voice and image extraction techniques, achieving a high accuracy rate of 95.52%. This game offers an engaging and interactive method for learning Arabic, a language often considered challenging. The incorporation of Artificial Neural Network technology enhances the effectiveness of the learning game, providing users with a unique and innovative language learning experience. By combining voice and image extraction techniques, the game offers a comprehensive approach to enjoyably improving Arabic speaking skills.
Study of Bitcoin Market Efficiency Using Runs Test and Autocorrelation Sukmana, Husni Teja; Khairani, Dewi
Journal of Current Research in Blockchain Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i1.9

Abstract

This paper presents a comprehensive statistical analysis of Bitcoin's daily returns, focusing on their unique characteristics and implications for financial modeling and market behavior. The descriptive statistics reveal a mean daily return of 0.001912 and a standard deviation of 0.044069, highlighting high volatility. The skewness of -1.297892 and kurtosis of 22.099740 indicate a left-skewed, leptokurtic distribution with frequent extreme price movements. The Jarque-Bera test statistic of 95428.68, with a p-value of 0.0, strongly rejects the null hypothesis of normality, suggesting that traditional financial models assuming normally distributed returns may be inappropriate for Bitcoin. The ADF test statistic of -12.303, with a p-value of 7.36e-23, confirms the stationarity of Bitcoin's daily returns, validating their suitability for time series analysis techniques such as ARIMA and GARCH models. Autocorrelation analysis uncovers significant short-term predictability in Bitcoin returns, challenging the weak form of market efficiency, though this predictability diminishes over time. The Runs Test, with a z-score of 2.56 and a p-value of 0.01, further supports the presence of short-term non-random behavior. Additional visualizations, including the daily closing price plot, histogram, and boxplot of daily returns, illustrate the high volatility and substantial variability in Bitcoin's market behavior. The findings underscore the need for specialized risk management strategies and financial models tailored to the cryptocurrency market's unique dynamics. While Bitcoin offers opportunities for high returns, it also poses significant risks due to its volatile nature and frequent extreme price movements. Future research should explore advanced models accounting for heavy tails and volatility clustering and examine the impact of external factors such as regulatory changes and macroeconomic events on Bitcoin's statistical properties. Understanding these characteristics is crucial for informed investment decisions and effective trading strategies in the evolving cryptocurrency market.
Co-Authors Abraham Zakky Zulhazmi Abu Bakar, Normi Sham Awang Ambran Hartono Amenobelia Sitepu Amrullah, Faiq Fawwaz Andi Faisal Bakti & Venny Eka Meidasari Andria Farhan Annisa Daffa Oktriyana Antoro, Fauzan Farhan Arini Arini Arini Arini Aulia, Atqia Bisri Nursa Fadillah Dede Rosyada Diah Setiawati Ellrica Dewi Herawati Jana Fachry Abda El Rahman Fadillah, Bisri Nursa Fauzan Farhan Antoro Fitri Mintarsih Haryono, Ahmad Fauzan Hendra Bayu Susueno Husni Teja Sukmana Imam Luthfi Rahmatullah Imam Tri Wibowo Imelda Sari Izwita Dewi I’im Umamil Khoiri Khodijah Hulliyah Kusmana Kusmana Lestania Simatupang Manullang, Thomas Ridoansih Melly Br Bangun Mian, Mian Miftakhul Nuuril Azizah Muhammad Ammaridho Romdhan Siregar Muhammad Iqbal Naibaho, Yuni Naftalia Nashrul Hakiem Nenny Anggraini, Nenny Noeni Indah Sulistiya Nurhayati Nurlatifah Nurlatifah Nurul Faizah Rozi Nurul Faizah Rozy Nurwijayanti Oh, Lee Kyung Pebrija, Putri Rahmatullah, Imam Luthfi Rena Latifah Rizal Broer Bahaweres Rizka Amalia Putri Rozi, Nurul Faizah Rozy, Nurul Faizah Saepul Aripiyanto Sahat Saragih SANI SUSANTI Sembiring, Putri Pebrija Sianturi, Mian Sinaga, Jelita Tessalonika Siti Ummi Masruroh Sri Yunita Subaedah, Sitti Subchi, Imam Sulistiya, Noeni Indah Supardi Razak Tabah Rosyadi Tabah Rosyadi Tessalonika, Jelita Viva Arifin Widiyanti, Nur Febriana Winda Tarihoran Wisnu Ari Mukti Yusnadi, Yusnadi Zawawi, Khoirunnisya Zulkifli Zulkifli