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All Journal Jurnal CoreIT Krea-TIF: Jurnal Teknik Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Jurnal Daur Lingkungan Jurnal Computer Science and Information Technology (CoSciTech) Journal of Community Development JURNAL ILMU DAN TEKNOLOGI KESEHATAN TERPADU Malcom: Indonesian Journal of Machine Learning and Computer Science JURNAL SANITASI LINGKUNGAN Journal Of Artificial Intelligence And Software Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Iwan Iskandar
Jurusan Kesehatan Lingkungan, Poltekkes Kemenkes, Tanjungpinang, Kepulauan Riau,, 29124
Computer Science & IT Control & Systems Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Nursing Public Health Social Sciences

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Pembuatan Chatbot Telegram untuk Layanan Pencarian Al-Quran Lawami, Reza Haya; Harahap, Nazruddin Safaat; Yusra; Iskandar, Iwan
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 2 (2024): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i2.761

Abstract

The use of information technology in everyday life has become commonplace in today's society and is increasingly expanding into various fields, including access to the Qur'an. Although many digital Qur'an applications are available, a more flexible and interactive feature is still needed. The research focuses on developing a Telegram chatbot to facilitate the search for the Qur'an. The waterfall approach is used in developing this chatbot system, including needs analysis, design, implementation, and testing. Data collected through observation was used to understand the weaknesses of the existing Al-Qur'an chatbot system and formulate the desired functional requirements of the bot. This chatbot allows users to search for Qur'anic verses based on surah, verse, keyword, and voice recording. User Acceptance Test (UAT) testing showed a high level of acceptance from users, with 88.57% of respondents strongly agreeing with the chatbot's functionality and performance. Thus, this Al-Qur'an search service Telegram chatbot successfully provides a new alternative to accessing and understanding the Al-Qur'an and helps Muslims and Al-Qur'an memorizers in the process of searching and understanding the verses more efficiently.
HUBUNGAN PENGETAHUAN, SIKAP DAN TINDAKAN TENTANG PENYAKIT DEMAM BERDARAH DENGUE (DBD) DENGAN RUMAH POSITIF JENTIK WARGA RT. 003 RW.002 KEL. TEBING KEC. TEBING KABUPATEN KARIMUN: The Relationship of Knowledge, Attitude and Action about Dengue Hemorrhagic Fever (DHF) with Larvae Positive House from Residents of RT. 003 RW. 002 In Kelurahan Tebing, Karimun Regency Samosir, Kholilah; rizkaramdhaniartie, siti; Iskandar, Iwan; Herdiana, Dora
JURNAL ILMU DAN TEKNOLOGI KESEHATAN TERPADU Vol. 1 No. 2 (2021): Jurnal Ilmu dan Teknologi Kesehatan Terpadu
Publisher : Poltekes Kemenkes Tanjungpinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53579/jitkt.v1i2.30

Abstract

The Incidence Rate of Dengue Hemorrhagic Fever (DHF) in Karimun Regency was 92.6 per 100,000 population. Tebing sub-district is the sub-district with the highest number of dengue fever cases, as many as 108 cases, 28 cases of which are in the Tebing sub-district. The purpose of this study was to determine the relationship between knowledge, attitudes and actions of residents about dengue disease with larva positive homes in the RT. 003 RW. 002 in Tebing Village, Karimun Regency in 2021. This research was an analytical study with a cross-sectional approach. The number of samples as many as 98 respondents. There was 32 respondents (32.7%) had good knowledge, there were 50 respondents (51%) had a good attitude, and 19 respondents (19.4%) had good actions against dengue disease. Houses with positive dengue mosquito larvae as many as 23 (23.5%). There was no relationship between knowledge and larvae positive house (Pvalue 0.959 > 0.05) and attitude with larva positive house (P value 0.336 > 0.05). However, the action against DHF was associated with positive larvae (Pvalue 0.000 < 0.05). People should do 3M Plus activities more often and keep their houses clean regularly, not store used goods that have the potential to breed mosquitoes, and sprinkle abate powder.
Pendampingan Mahasiswa Sebagai Agents of Change: Mendorong Penerapan Teknologi Informasi Yang Beretika dan Berlandaskan Nilai-Nilai Islam Afriyanti, Liza; Iskandar, Iwan; Sandi, Wahyu Ari
Journal of Community Development Vol. 5 No. 2 (2024): December
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v5i2.1371

Abstract

The development of digital literacy is very important in facing the current digital era. In the educational context, digital literacy is the foundation that enables the learning process to be more interactive, dynamic and in line with current developments. This shows that digital literacy is not only relevant in everyday life, but also has a significant impact in the world of education, especially at the tertiary level. Students' digital literacy skills are an important aspect in facing the demands of today's digital era. However, just having information and communication technology skills is not enough, students are also required to understand, use and utilize digital technology effectively, responsibly and ethically with character and based on Islamic values. The form of service activity is a seminar which is held online via the zoom meeting application with the presentation method. To assess the achievement of goals and success of this service activity, the following indicators can be used as benchmarks, namely indicators of goal achievement and success benchmarks. By combining the two indicators above, it can provide a comprehensive picture of the achievement of objectives and the success of Community Service activities that have been carried out. Based on the evaluation results obtained from the reporting stage, it is known that this activity has various advantages and does not experience difficulties during its implementation, as well as assisting students as agents of change in the application of information technology can be carried out more ethically and based on Islamic values.
THE INFLUENCE OF MAJOR EXPERTISE COURSES ON ALUMNI EMPLOYMENT USING THE APRIORI METHOD Irsyad (Scopus ID: 57204261647), Muhammad; Iskandar, Iwan; Gusti, Siska Kurnia
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i2.34144

Abstract

The role of alumni in university progress and quality is vital. This study used data from the tracer study application to analyze the relationship between skill courses and alumni employment. The data mining technique of association was employed to find linkages between different parameters. The Apriori algorithm was used to identify patterns that described the relationship between skill courses and alumni employment. The findings revealed that the most sought-after professions by alumni of the Informatics Engineering Study Program were educators, such as teachers and lecturers, with a support value of 18.7692%. Programmers were also in high demand, with a support value of 15.3846%. The subjects that were found to have the greatest influence on employment were Database, Computer Network, Computer Human Interaction, and Software Engineering. These findings provide valuable insights for the Informatics Engineering Study Program to prioritize and enhance these influential courses in terms of curriculum, teaching methods, and teaching materials, with the aim of improving the relevancy and quality of the courses in supporting alumni employment.
Applying Local Interpretable Model-agnostic Explanations (LIME) for Interpretable Deep Learning in Lung Disease Detection Ananda, Sherly; Negara, Benny Sukma; Irsyad, Muhammad; Jasril, Jasril; Iskandar, Iwan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.7042

Abstract

Artificial Intelligence (AI) semakin banyak diterapkan dalam bidang kesehatan melalui model Machine Learning (ML) dan Deep Learning (DL). Namun, kompleksitas model modern yang bersifat black-box menimbulkan kebutuhan akan metode interpretasi yang transparan. Explainable AI (XAI) hadir untuk menjembatani hal tersebut, dengan memberikan pemahaman yang lebih baik terhadap kinerja model. Penelitian ini mengimplementasikan metode Local Interpretable Model-agnostic Explanations (LIME) untuk memvisualisasikan hasil klasifikasi model DL berbasis arsitektur ResNet18 terhadap citra Chest X-ray (CXR) pada tiga kelas: normal, COVID-19, dan pneumonia. Model mencapai precision, recall, dan F1-score rata-rata sebesar 97%, serta Accuracy sebesar 98%. Visualisasi LIME menunjukkan area citra yang berkontribusi signifikan terhadap klasifikasi, serta mampu membedakan ketiga kelas dengan baik. Hasil ini mendukung penggunaan XAI untuk meningkatkan interpretabilitas model DL dalam diagnosis medis.
Interpreting Lung Disease Detection from Chest X-rays Using Layer-wise Relevance Propagation (LRP) Fauziyyah, Laila Nurul; Negara, Benny Sukma; Irsyad, Muhammad; Iskandar, Iwan; Yanto, Febi
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.7043

Abstract

Penelitian ini mengusulkan pendekatan klasifikasi penyakit paru berbasis citra X-ray menggunakan arsitektur VGG16 yang dilengkapi metode interpretabilitas Layer-wise Relevance Propagation (LRP). Dataset terdiri dari tiga kelas: COVID-19, pneumonia, dan normal, yang diproses melalui augmentasi dan normalisasi. Model dilatih dengan rasio data 70:30, learning rate 0.001, batch size 32, dan optimizer Adam. Hasil pelatihan menunjukkan akurasi tinggi sebesar 96,78% dengan nilai precision, recall, dan F1-score yang seimbang. Metode LRP digunakan untuk menyoroti area penting pada citra yang berkontribusi terhadap prediksi model, sehingga meningkatkan transparansi keputusan. Kontribusi utama penelitian ini adalah integrasi VGG16 dengan LRP dalam klasifikasi multi-kelas citra X-ray, yang memberikan hasil akurat sekaligus interpretasi visual yang mendukung kepercayaan dalam aplikasi medis.
Application of Shapley Additive Explanations (SHAP) in Deep Learning for Lung Disease Detection Using X-ray Images Muliani, Sarifah; Negara, Benny Sukma; Irsyad, Muhammad; Jasril, Jasril; Iskandar, Iwan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.7044

Abstract

Pemeriksaan menggunakan citra x-ray merupakan metode yang efektif dalam membantu deteksi penyakit paru-paru, seperti COVID-19, dan pneumonia. Seiring dengan perkembangan teknologi yang meningkat, proses diagnosis kini dapat dilakukan secara lebih akurat dengan memanfaatkan sistem berbasis kecerdasan buatan. Salah satu metode yang banyak digunakan adalah deep learning namun metode ini bersifat black-box, sehingga hasil prediksi sulit dipahami dengan alasan dibalik keputusan model. Tujuan penelitian ini adalah untuk membangun sistem klasifikasi citra x-ray menggunakan model deep learning berbasis Convolutional Neural Network (CNN) dengan arsitektur VGG-16, serta menerapkan metode Shapley Additive Explanations (SHAP) untuk memberikan penjelasan mengenai visual terkait area citra yang mempengaruhi hasil prediksi. Model dilatih menggunakan beberapa konfigurasi, dan hasil terbaik diperoleh pada rasio data 80% : 20%, learning rate 0.001, batch size 32, dan 50 epoch. Hasil penelitian menunjukkan bahwa model mampu mencapai akurasi sebesar 95,75% pada data training dan 96,00% pada data validasi. Metode SHAP digunakan untuk meningkatkan pemahaman terhadap hasil prediksi. Hasil menunjukkan bahwa kombinasi deep learning dan SHAP mampu memberikan penjelasan visual terhadap hasil prediksi model.
Lung Disease Detection Using Gradient-Weighted Class Activation Mapping (Grad-CAM) Sofiyah, Wan; Negara, Benny Sukma; Irsyad, Muhammad; Iskandar, Iwan; Yanto, Febi
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.7041

Abstract

Early detection of respiratory diseases such as Coronavirus Disease-19 (Covid-19) and Pneumonia is crucial for accelerating treatment and preventing more serious complications. This study proposes a method for classifying Chest X-ray (CXR) images using a Convolutional Neural Network (CNN) to distinguish between Covid-19, Pneumonia, and normal lungs. Model training involved exploring various hyperparameter combinations to find the optimal configuration. The best results were achieved with a learning rate of 0.001, 50 epochs, and a batch size of 32, yielding an accuracy of 96.33%. Evaluation was conducted using accuracy, precision, recall, F1-score, and confusion matrix metrics. This study uses Gradient-Weighted Class Activation Mapping (Grad-CAM) as a transparent interpretation tool for model decisions. The main contribution of this study is the application of Grad-CAM in multi-class CXR classification to enhance model interpretability in lung disease diagnosis.
Perbandingan Performa Random Forest dan Long Short-Term Memory dalam Klasifikasi Teks Multilabel Terjemahan Hadits Bukhari: Comparison of Random Forest and Long Short-Term Memory Performance in Multilabel Text Classification of Bukhari Hadith Translation Ahmad, Rizmah Zakiah Nur; Harahap, Nazruddin Safaat; Agustian, Surya; Iskandar, Iwan; Sanjaya, Suwanto
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2046

Abstract

Hadits merupakan fondasi utama kedua dalam Islam, yang memandu umat Islam dalam menafsirkan nilai-nilai Islam dan mengimplementasikannya secara nyata dalam berbagai aspek kehidupan. Salah satu perawi hadits yang paling dihormati adalah Imam Bukhari, yang dikenal dengan ketelitian dan ketegasannya dalam memilih hadits-hadits yang otentik. Penelitian ini menggunakan data dari terjemahan hadis dari Sahih Bukhari ke dalam bahasa Indonesia yang telah diklasifikasikan ke dalam tiga kategori utama, yaitu anjuran, larangan, dan informasi. Untuk mengidentifikasi karakteristik masing-masing kategori, klasifikasi teks dilakukan dengan menggunakan dua metode populer, yaitu Random Forest (RF) dan Long Short-Term Memory (LSTM), yang dikenal efektif dalam memproses data teks berskala besar dan kompleks. Tujuan dari penelitian ini adalah untuk menguji perbedaan kinerja antara kedua metode tersebut dalam mengelompokkan hadis yang datanya telah lengkap. Hasil evaluasi menunjukkan bahwa metode RF mencapai akurasi tertinggi sebesar 89,48%, sedikit lebih unggul dari LSTM yang memperoleh 88,52%. Kedua metode mencatat nilai Hamming Loss yang sama, yaitu 0,1048 (89,52%). Temuan ini menunjukkan bahwa kelengkapan dan kualitas data hadis Bukhari berkontribusi dalam meningkatkan akurasi klasifikasi dengan memberikan konteks dan variasi yang lebih baik untuk model.
Perbandingan Performa Metode Klasifikasi Teks Multilabel Hadis Terjemahan Bukhari Menggunakan Support Vector Machine dan Long Short Term Memory: Performance Comparison of Multilabel Text Classification Methods on Translated Hadiths of Bukhari Using Support Vector Machine and Long Short Term Memory Ramadhani, Aulia; Safaat, Nazruddin; Agustian, Surya; Iskandar, Iwan; Sanjaya, Suwanto
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2051

Abstract

Hadis merupakan sumber hukum kedua dalam Islam, dan salah satu kitab hadis yang paling dikenal adalah Shahih al-Bukhari. Untuk mendukung pemahaman dan pengamalan yang tepat, hadis perlu diklasifikasikan secara akurat. Mengingat satu hadis dapat mengandung lebih dari satu informasi, pendekatan klasifikasi multilabel menjadi sangat relevan. Penelitian ini bertujuan untuk memberikan kontribusi dalam bidang klasifikasi teks dengan mengeksplorasi kombinasi metode dan parameter yang optimal untuk klasifikasi multilabel hadis. Hasil penelitian menunjukkan bahwa Support Vector Machine (SVM) memberikan performa terbaik pada label Larangan dengan Macro F1-score sebesar 82,57%, melalui kombinasi SVM + TF-IDF menggunakan kernel = linear, parameter C (regularization parameter) = 1 tanpa stopword removal dan tanpa balancing. Sementara itu, Long Short Term Memory (LSTM) juga unggul pada label Larangan dengan Macro F1-score 82,66% pada kombinasi parameter Epoch = 20, Dropout = 0.5, Dense = 128 dan Batch Size = 64 tanpa stopword removal dan tanpa balancing kombinasi ini juga menghasilkan nilai Hamming Loss terendah sebesar 10,452%, yang lebih baik dibandingkan dengan penelitian sebelumnya serta menunjukkan bahwa LSTM terbukti lebih efektif secara keseluruhan dengan penyetelan parameter yang tepat. Penelitian ini juga berkontribusi dalam peningkatan kualitas data dengan melengkapi matan hadis yang digunakan, sehingga menghasilkan performa klasifikasi yang lebih baik.
Co-Authors Afriyanti, Liza Agustian, Surya Agustina, Auliyah Ahmad, Rizmah Zakiah Nur Alvinur Hidayat, Alvinur Amelia, Felina Ananda, Sherly Arasy, Abdurrahman Aulia Ramadhani Elin Haerani Elvia Budianita Fadhilah Syafria Fauziyyah, Laila Nurul Febi Yanto, Febi Fitri Insani Gusti, Siska Kurnia Handayani, Lala Syafitri Herdiana, Dora Idris, M. Fadhil Irsyad (Scopus ID: 57204261647), Muhammad Jasril Jasril Kholilah Samosir Lawami, Reza Haya Lestari Handayani Mai Candra, Reski MF, M.Yusuf Muhammad Affandes, Muhammad Muhammad Irsyad Muliani, Sarifah Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Negara, Benny Sukma Permata, Rizkiya Indah Purnama, Irgi Putri. P, Zurneli Kurnia Reski Mai Candra Ridho Hilmawan, Ridho rizkaramdhaniartie, siti Sandi, Wahyu Ari Sofiyah, Wan Suwanto Sanjaya Tri Prastio Nugroho Yuli Novita Sari, Yuli Novita Yusra Yusra, Yusra Zulya Erda