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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Pekommas Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat Indonesian Journal of Electrical Engineering and Computer Science PINISI Discretion Review
Zulkifli Tahir
Universitas Hasanuddin
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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Early skin disease diagnosis by using artificial neural network for internet of healthcare things Wan Bejuri, Wan Mohd Yaakob; Mohamad, Mohd Murtadha; Tang, Michelle; Ahmad Khair, Aina Khairina; Adriyansyah, Yusuf Athallah; Kasmin, Fauziah; Tahir, Zulkifli
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1032-1041

Abstract

Internet of healthcare things (IoHT) represents a burgeoning field that leverages pervasive technologies to create technology driven environments for healthcare professionals, thereby enhancing the delivery of efficient healthcare services. In remote and isolated areas, such as rural communities and boarding schools, access to healthcare professionals (especially dermatologists) can be particularly challenging. However, these areas often lack the specialized expertise required for effective skin disease consultations. Thus, the purpose of this research is to design a scheme of early skin disease diagnosis for internet of healthcare things that is accessible anywhere and anytime. In this research, the image of skin disease from patient will be taken by using a mobile phone for predicting and identifying the disease. This proposed scheme will diagnose skin disease and convert it be meaningful information. As a result, it show our proposed scheme can be the most consistent in term of accuracy and loss compared to others method. Overall, this research represents a significant step toward improving healthcare accessibility and empowering individuals to manage their own health. Furthermore, the proposed scheme is anticipated to contribute significantly to the IoHT field, benefiting both academia and societal health outcomes.
Socialization of Buginese Language E-learning Application with Lontara Script Translation Feature at SMPN 2 Pangkajene Nurtanio, Ingrid; Yohannes, Christoforus; Bustamin, Anugrayani; Mokobombang, Novy Nur R A; Areni, Intan Sari; Tahir, Zulkifli; Adnan, Adnan; Marindah, Tyanita Puti; Paundu, Ady Wahyudi; Nurdin, Arliyanti; Musyfirah, Kamtina; Hikmah, Nur; Mahdaniar, Mahdaniar
JURNAL TEPAT : Teknologi Terapan untuk Pengabdian Masyarakat Vol 7 No 2 (2024): Kolaborasi yang Kuat untuk Kekuatan Kemasyarakatan
Publisher : Faculty of Engineering UNHAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25042/jurnal_tepat.v7i2.528

Abstract

Preservation of indigenous languages, especially Bugis with Lontara script, is an important challenge in the digital era. In school activities, mastery and learning of local languages ​​are associated with one subject, namely Muatan Lokal. However, the main problem that is often encountered is the use of indigenous languages ​​that are increasingly minimally socialized, thus reducing students' interest and motivation in learning this local language, especially in SMP Negeri 2 Pangkajene Class VII. This community service aims to be a forum for socializing research results in the Department of Informatics and Electrical Engineering, Hasanuddin University in the form of an E-learning application that facilitates interactive learning of Bugis with Lontara script. In addition, this activity is expected to contribute to the advancement of knowledge and technology by providing E-learning tools, in the form of mobile applications, to support learning both at school and at home. The process of introducing this application involves quantitative analysis in the form of an initial survey (pre-test) which includes the user experience (in this case students) when learning Bugis and then ends with a final survey (post-test) and System Usability Scale (SUS) testing to determine the student's experience when using this Bugis language application. The results obtained indicate the formation of Bugis language learning motivation after participants are familiar with this E-learning application with an increase of 52% from 32% less motivated to 84% very motivated. In addition, this Bugis language E-learning application also reached an acceptable level based on SUS with a value of 74.
Comparison of Convolutional Neural Network Methods for the Classification of Maize Plant Diseases Abas, Mohamad Ilyas; Syafruddin Syarif; Ingrid Nurtanio; Zulkifli Tahir
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 1 (2024): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i1.3656

Abstract

The focus of this study is the classification of maize images with common rust, gray leaf spot, blight, and healthy diseases. Various models, including ResNet50, ResNet101, Xception, VGG16, and ENet, were tested for this purpose. The dataset used for corn plant diseases is publicly available, and the data were split into separate sets for training, validation, and testing. After processing the data, the following models were identified: the Xception model epoch with an accuracy of 83.74%, the ResNet model with an accuracy of 97.19% at epoch 8/10, the ResNet101 model with an accuracy of 97.55% at epoch 10/10, and the ENet model with an accuracy of 98.69% at epoch 9/1000. ENet exhibited the highest accuracy among the five models at 98.69%. Additionally, ENet achieved an average accuracy of 95.45%, the highest among all tested models, based on the average accuracy in the confusion matrix. This research indicates that ENet performs best at processing data related to maize plant diseases. Consequently, the analysis of maize plant diseases is expected to evolve as a result of this research. Following the implementation of the system's generated model, this research will continue to explore its impact. The intention is to provide a summary of the comparative classification performance of CNN algorithms.
Entity Extraction in Indonesian Online News Using Named Entity Recognition (NER) with Hybrid Method Transformer, Word2Vec, Attention and Bi-LSTM Zainuddin, Zahir; Mudassir, -; Tahir, Zulkifli
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2902

Abstract

Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP) that identifies entities such as person names, locations, and organizations within the text. While many NER studies have concentrated on the English language, there is a significant need for further research on Indonesian NER. Indonesia presents unique challenges due to its structural complexities, polysemy, and ambiguities. Conventional machine learning and deep learning techniques have been widely applied in NER; however, more detailed exploration into integrating these methods for performance improvement is needed. This study introduces a novel hybrid model, TWBiL, which combines Transformer mechanisms, Word2Vec embeddings, Bidirectional Long Short-Term Memory (Bi-LSTM), and Attention mechanisms to enhance NER performance on Indonesian text. TWBiL harnesses the strengths of each component to generate superior word vector representations, extract intricate sentence features, and disambiguate entities contextually. Our experimental results demonstrate the effectiveness of the proposed hybrid model, revealing a significant improvement in NER performance. Specifically, TWBiL achieves an F1-Score of 85.11 on an Indonesian online news dataset, outperforming the traditional Bi-LSTM model, which achieved a score of 75.18. The results indicate that TWBiL effectively reduces ambiguity and captures context more accurately, enhancing entity recognition. Future research should priorities reducing computational time when handling larger datasets without compromising overall NER performance. This study underscores the potential of integrating advanced deep learning techniques to tackle the unique challenges of Indonesian NER, thus providing a solid foundation for further advancements in the field.
Implementation of the Application of Manuscript Procedures in the Public Company Logistics of Palopo City Kurra, Sudiarti Dewi; Tahir, Zulkifli
PINISI Discretion Review Volume 9, Issue 1, September 2025
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/pdr.v1i1.80808

Abstract

This research is a Qualitative Descriptive research that aims to determine the extent of the Implementation of Official Document Guidelines at the Perum Bulog Palopo City Office. The primary data sources in this study were obtained using Observation, interview and Documentation techniques so that the data sources and informants amounted to 3 people, namely; Head of the Secretariat, General and Public Relations Section, Staff of the Correspondence Section. And Staff of the R / R Section. The results of this study indicate that the Implementation of Official Document Guidelines at the Perum Bulog Palopo City Office has been attempted to be implemented well and is still undergoing adjustments in its implementation. This is seen using the principles of Efficiency, Standardization, Accountability, Relatedness, Speed and Accuracy, and Security which have fulfilled almost all aspects of the six principles.
Co-Authors A. Ais Prayogi Alimuddin A.Ais Prayogi Adnan - Adnan Adnan Adnan Adnan Adnan Adnan Adriyansyah, Yusuf Athallah Ady Wahyudi Paundu Ady Wahyudi Paundu Ahmad Khair, Aina Khairina Ais Prayogi Alimuddin Amil Ahmad Ilham Amil Ahmad Ilham Andani Ahmad Anugrahyani - Anugrayani Bustamin Anugrayani Bustamin Anugrayani Bustamin Areni, Intan Sari Aswad, Iqra Aulia Darnilasari Bustamin, Anugrayani Christoforus Yohanes elly warni Elly Warni Indra Bayu Indrabayu - Indrabayu Indrabayu Ingrid Nurtanio Intan Sari Areni Intan Sari Areni Iqra Aswad Iqra Aswad Kasmin, Fauziah Kurra, Sudiarti Dewi Mahdaniar, Mahdaniar Marindah Wardhani, Tyanita Puti Marindah, Tyanita Puti Mohamad Ilyas Abas Mohamad, Mohd Murtadha Mokobombang, Novy Nur R A Mudassir, - Muh. Alief Fahdal Imran Oemar Muhammad Niswar Muhammad Nizwar Mukarramah Yusuf Mukarramah Yusuf Musyfirah, Kamtina nfn Syafaruddin Nur Hikmah Nur Indha Mulyasari Nurdin, Arliyanti Paundu, Ady Wahyudi Rasyid, Muhammad Rafli Rieka Zalzabillah Putri Riny Yustica Dewi Rizka Irianty Syafaruddin, nfn Syafruddin Syarif Tang, Michelle Wan Bejuri, Wan Mohd Yaakob Wardi Wardi Yohannes, Christoforus Zaenab - Zaenab Muslimin Zaenab Muslimin Zahir Zainuddin Zahir Zainuddin