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All Journal TEKNIK INFORMATIKA Jurnal Indo-Islamika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Manajemen Pendidikan Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal CoreIT Applied Information System and Management MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Sosiologi Agama Building of Informatics, Technology and Science Journal of Applied Data Sciences International Journal Of Computer, Network Security and Information System (IJCONSIST)
Nashrul Hakiem
Universitas Islam Negeri Syarif Hidayatullah Jakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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A Comparative Analysis of Random Forest, XGBoost, and LightGBM Algorithms for Emotion Classification in Reddit Comments Anggraini, Nenny; Putra, Syopiansyah Jaya; Wardhani, Luh Kesuma; Arif, Farid Dhiya Ul; Hakiem, Nashrul; Shofi, Imam Marzuki
JURNAL TEKNIK INFORMATIKA Vol. 17 No. 1: 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.v17i1.38651

Abstract

This research aims to compare the performance of three classification algorithms, namely Random Forest, XGBoost, and LightGBM, in classifying emotions in Reddit comments. Emotion classification in Reddit comments is a complex classification problem due to its numerous variations and ambiguities. This research utilizes the GoEmotions Fine-Grained dataset, filtered down to 7,325 Reddit comments with 5 different basic emotion labels. In this study, data preprocessing steps, feature extraction using CountVectorizer and TF-IDF, and hyperparameter tuning using GridSearchCV for each algorithm are conducted. Subsequently, model evaluation is performed using Cross-Validation and confusion matrix. The results of the study indicate that Random Forest outperforms the XGBoost and LightGBM algorithm with an accuracy of 75.38% compared to XGBoost with 69.05% accuracy and LightGBM with 66.63% accuracy.
Nearest Neighbor Interpolation and AES Encryption for Enhanced Least Significant Bit (LSB) Steganography Anggraini, Nenny; Wardhani, Luh Kesuma; Assyahid, Muhammad Hudzaifah; Hakiem, Nashrul; Yusuf, Muhammad; Setyawan, Okky Bagus
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7079

Abstract

The increasing use of digital communication raises concerns about data security, especially when transmitting sensitive information. Steganography conceals messages within digital media to prevent detection. However, conventional methods face storage limitations, leading to message truncation or distortion, making hidden messages more detectable. This study proposes a combination of Nearest Neighbor Interpolation (NNI) and Least Significant Bit (LSB) steganography to dynamically expand the cover image, allowing larger encrypted messages to be embedded while maintaining image integrity. NNI was chosen over other interpolation techniques such as Bilinear and Bicubic due to its lower computational complexity and preservation of sharp edges, which minimizes blurring artifacts that could make steganographic alterations more noticeable. AES-128 encryption ensures message confidentiality before embedding. The system was developed as a web-based application to improve usability. The research followed the Waterfall Software Development Life Cycle (SDLC), and Black Box Testing validated system functionality. Testing results showed that the method successfully embedded and extracted messages without data loss, maintaining PSNR values above 40 dB, ensuring minimal perceptual distortion. However, the maximum interpolation limit was 5310 × 5310 pixels, beyond which system constraints caused failures. The stego-images retained original aspect ratios, reducing suspicion. Despite its success, the system remains vulnerable to modifications such as color changes, cropping, rotation, and compression, which can disrupt the message.
Acceptance and Success Model for AI Use in Higher Education: Development, Instrument Decomposition, and Its Triangulation Testing Subiyakto, Aang; Huda, Muhammad Q; Hakiem, Nashrul; Suseno, Hendra B; Arifin, Viva; Azmi, Agus N; Sani, Asrul; Yuniarto, Dwi; Hartawan, Muhammad S; Suryatno, Agung; Muji, Muji; Kurniawan, Fachrul; Kusumawati, Ririen; Balogun, Naeem A; Ahlan, Abd. Rahman
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.619

Abstract

Prior social computing studies described that the performance of technology products is about how the product use benefits the users, including Artificial Intelligence (AI). To have an impact, ensuring how AI is used is a prerequisite after the development. Furthermore, its use is also influenced by how users accept AI. This study aimed to develop an acceptance and success model of AI use in the higher education world from the user perspective, to decompose the model into its instrument level, and to test the validity and reliability of the research instrument. The researchers developed the model by adopting and combining the Technology Acceptance Model (TAM) and the Information System Success Model (ISSM) and adapting the proposed model in the context of AI use in higher education learning. The measurement items were derived from definitions of the variables and indicators of the model. The instrument was tested sequentially using triangulation methods. The quantitative testing was online survey with about 51 respondents and the qualitative one was interview involving five experts. This study may contribute methodologically as one of the guidance for novice scholars in similar works. It may relate to the clarity of the research procedure and the implementation of the mixed testing methods. Of course, the assumptions, samples, and data used in the study cannot be generalized for the other studies. Referring to the model development, the proposed model may not cover the other factors related to the ethical, cultural, and organizational barriers for adopting AI. These barriers may also affect its acceptance and success. Thus, the adoption of the factors related the barriers may also be interesting to study further.
PENGUATAN LITERASI DIGITAL GURU MELALUI PENERAPAN EDUNAV: STUDI KASUS SEKOLAH KHARISMA BANGSA Faiza, Fikis Silmi; Ratnaningsih, Siti; Hakiem, Nashrul
Jurnal Manajemen Pendidikan Vol. 10 No. 4 (2025): Regular Issue (In Progress)
Publisher : STKIP Pesisir Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34125/jmp.v10i4.1326

Abstract

This study aims to analyze the implementation of Edunav in improving teachers' digital literacy and its impact on learning transformation at Sekolah Kharisma Bangsa. Using a descriptive qualitative approach and content analysis techniques, this study explores the Edunav adoption process starting from socialization, training, use of core features, to its integration in management and learning practices. The findings of the study show that Edunav is able to strengthen the four dimensions of teachers' digital literacy technical, pedagogical, informational, and ethical which further encourages improvement in the quality of learning planning, evaluation, and data-based decision-making through learning analytics. The implementation of Edunav is also influenced by various supporting factors, such as infrastructure readiness, management support, collaborative culture, and ongoing training programs. However, a number of challenges remain, such as digital literacy gaps between teachers, technical barriers, and resistance to pedagogical changes. Theoretical analysis based on multidimensional digital literacy and the TPACK framework show that the effectiveness of Edunav is highly dependent on teachers' ability to integrate technology with pedagogical strategies and learning content. This research emphasizes the importance of targeted professional development, consistent digital policies, and enhancement of LMS features to support the digital transformation of education in a sustainable manner.
Machine Learning for Password Strength Classification Using Length and Entropy Tanjung Arswendo Yudha; Reyhan, Muhamad; Mutmainnah, Dianisa; Hakiem, Nashrul
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.139

Abstract

Password security is a critical cybersecurity challenge due to the prevalence of user-generated weak credentials, so automated evaluation methods are needed. This paper develops a Random Forest classification model to predict password strength based on two main features, namely password length and Shannon entropy, trained on a large-scale public dataset. The model achieved a classification accuracy of 91.5% on the test data, where feature importance analysis identified entropy as the most significant predictor. The resulting high-accuracy model is suitable for integration into real-time password strength feedback systems and provides a quantitative basis for formulating stronger security policies.
This Detection of Hate Speech in Social Media Using Machine Learning Akbar, Amin Kurniawan; Ridha, Afif Nabil; Muthmainnah, Ami Chandra; Irsyad, Muhammad; Hakiem, Nashrul; Broer, Rizal
IJCONSIST JOURNALS Vol 5 No 2 (2024): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i2.142

Abstract

This paper addresses the critical issue of hate speech detection in social media, a growing concern given the widespread use of online platforms for communication and information dissemination. The proliferation of hate speech contributes to online harassment, discrimination, and the propagation of harmful ideologies, posing significant societal challenges. This study proposes a machine learning-based approach for identifying and classifying hate speech across various social media datasets. We leverage a comprehensive collection of parsed datasets, including those related to aggression, attack, toxicity, and specific instances from Twitter (general, racism, sexism), YouTube, and Kaggle. The methodology involves data preprocessing, feature extraction, and the application of machine learning algorithms to effectively distinguish hate speech from benign content. Our findings aim to contribute to the development of robust automated systems for content moderation, fostering safer and more inclusive online environments.
IoT-based Integrated System Portable Prayer Mat and DailyWorship Monitoring System Luh Kesuma Wardhani; Nenny Anggraini; Nashrul Hakiem; M. Tabah Rosyadi; Amin Rois
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3058

Abstract

Muslims have various difficulties in praying, such as difficulty memorizing the number of rak’ah they have been doing and determining the direction of the Qibla. In this research, we proposed a technological device for monitoring daily worship in Islam. We presented the IoT-based integrated system as a portable prayer mat serving as a rak’ah counter, Qibla direction finder, and a mobile worship monitoring system. A prototyping approach was used to produce a portable smart prayer mat, and Rapid Application Development was used to develop a mobile daily worship system. The device comprises an Arduino AT Mega 2560 powered portable prayer mat through a force-sensitive resistor sensor and an HMC 5883L compass module. The device sends the prayer activity to the worship applications in detail. The daily worship monitoring application itself has numerous features that enable users to track their daily worship activities, including the Hijri calendar, the time of compulsory prayers, the fulfillment of sunnah prayers, and fasting. Evaluation results showed that the system detected the rak’ah correctly in each cycle with average pressure to the FSR sensor of 81.36. The average time required to connect with a smartphone was 0.862 seconds. It also functions well as a Qibla finder. The black box testing results showed that the device and application performed effectively. It can send the worship data recapitulation to the application using Bluetooth.
Co-Authors AA Sudharmawan, AA Aang Subiyakto Abd. Rahman Ahlan Akbar, Amin Kurniawan Al Anziy, Misyal S Al Fath, Miftahul Kahfi Amin Rois Angga Kurniawan Anggraini, Nenny Arif, Farid Dhiya Ul Arini Arini Ariq Cahya Wardhana Asrul Sani Assyahid, Muhammad Hudzaifah Aulia, Atqia Azmi, Agus N Balogun, Naeem A Broer, Rizal Dana Indra Sensuse Dede Rosyada Denny Saputra Dwi Yuniarto Dzul Fadli Rahman Erma Suryani Evy Nurmiati Fachrul Kurniawan Faiza, Fikis Silmi Fetrina, Elvy Hartawan, Muhammad S Heni Pujiastuti Huda, Muhammad Q Huda, Muhammad Qomarul Imam M Shofi Khairani, Dewi Luh Kesuma Wardhani M. Tabah Rosyadi Meinarini Catur Utami Muhammad Irsyad Muhammad Yusuf Muji, Muji Muthmainnah, Ami Chandra Mutmainnah, Dianisa Nenny Anggraini, Nenny Nia Kumaladewi Ratnaningsih, Siti Reyhan, Muhamad Ridha, Afif Nabil Ririen Kusumawati Rizal Broer Bahaweres Rohmawati, Hanung Sito Sangsawang, Thosporn Sarika Afrizal Sarika Afrizal Sarika Afrizal, Sarika Setyawan, Okky Bagus Shofi, Imam Marzuki Suryatno, Agung Suseno, Hendra B Syopiansyah Jaya Putra Tanjung Arswendo Yudha Viva Arifin Yudhanta, Satya Yuni Sugiarti, Yuni Zawawi, Khoirunnisya Zulkifli Zulkifli