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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Semantik Techno.Com: Jurnal Teknologi Informasi JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System JURNAL MEDIA INFORMATIKA BUDIDARMA JTERA (Jurnal Teknologi Rekayasa) INOVTEK Polbeng - Seri Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Jurnal Sains Kebidanan Advance Sustainable Science, Engineering and Technology (ASSET) Aptekmas : Jurnal Pengabdian Kepada Masyarakat Community: Jurnal Pengabdian Pada Masyarakat Jurnal Rekayasa elektrika INOVTEK Polbeng - Seri Informatika Prosiding SENTIMETER : Seminar Nasional Teknologi Informasi, Mekatronika, dan Ilmu Komputer
Abu Salam
Universitas Dian Nuswantoro
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XGBoost and Random Forest Optimization using SMOTE to Classify Air Quality Arifianti, Fidela Putri; Salam, Abu
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.18136

Abstract

Air pollution due to the growth of industry and motorized vehicles seriously threatens human health. Clean air is essential, but pollutant contamination can cause acute respiratory illnesses and other illnesses. Several studies have been carried out to anticipate this air pollution. Various algorithms, methods, and data balancing techniques have been implemented, but still need to be done to obtain better accuracy results. Therefore, this study aims to classify heart disease using the XGBoost and Random Forest algorithms and implement the SMOTE technique to overcome data imbalance. This research produces a Random Forest algorithm with SMOTE implementation with splitting 80:20, which has the best accuracy with an accuracy of 92.4%, an average AUC of 0.98, and a log loss of 0.2366, which shows that SMOTE has succeeded in improving model performance in classifying minority classes. Based on the results obtained, the XGBoost and Random Forest algorithms after SMOTE are superior to the model with SMOTE, with accuracy above 90%.
REDUCING UNDER-FETCHING AND OVER-FETCHING IN REST API WITH GRAPHQL FOR WEB-BASED SOFTWARE DEVELOPMENT Muzaki, Rizki Nuzul; Salam, Abu
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1725

Abstract

Rest API is the most popular architectural style in website-based software development. However, Rest API has under-fetching and over-fetching problems. Under-fetching is a situation when the client has to make requests to several endpoints, while over-fetching is a situation when the client receives more data than needed. There is an alternative technology to Rest API, namely GraphQL. GraphQL has the potential to solve both under-fetching and over-fetching problems. This research aims to analyze how quickly GraphQL responds in overcoming under-fetching and over-fetching problems and conducting condition analysis to determine when it is best to use GraphQL. In this research, tests were conducted to answer these problems by applying each of the five test scenarios for under-fetching and over-fetching problems. Test results show that GraphQL can provide response speeds of 36.84% to 93.04% superior to Rest API. In the case of under-fetching, it is best to choose GraphQL when there is a need to call more than four endpoints. Meanwhile, for over-fetching problems, using the Rest API provides adequate response speed. However, if a more optimal response speed is needed, using GraphQL could be an alternative.
PERANCANGAN SISTEM PREDIKSI KELULUSAN MAHASISWA UNIVERSITAS DIAN NUSWANTORO MENGGUNAKAN UNIFIED MODELING LANGUAGE (UML): PERANCANGAN SISTEM PREDIKSI KELULUSAN MAHASISWA UNIVERSITAS DIAN NUSWANTORO MENGGUNAKAN UNIFIED MODELING LANGUAGE (UML) Zeniarja, Junta; Salam, Abu; Alan Ma’ruf, Farda
Prosiding Seminar Nasional Teknologi Informasi, Mekatronika, dan Ilmu Komputer Vol 1 (2022): Sentimeter 2022
Publisher : Prosiding Seminar Nasional Teknologi Informasi, Mekatronika, dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Mahasiswa ialah salah satu tiang yang sangat berarti dalam siklus hidup suatu Universitas. Jumlah kelulusan suatu Universitas sering kali mempunyai perbandingan yang kecil bila dibanding dengan jumlah mahasiswa yang diperoleh pada tahun akademik yang serupa. Tingkatan kelulusan mahasiswa yang kecil ini bisa disebabkan oleh sebagian aspek, seperti banyaknya aktivitas kemahasiswaan yang diiringi oleh aspek ekonomi, serta aspek-aspek lainnya. Perihal ini membuat sesuatu Universitas wajib mempunyai desain ataupun metode yang bisa memperhitungkan apakah mahasiswa itu bisa lulus dengan durasi yang sesuai. Salah satu faktor yang mendukung keberhasilan di dalam Univeritas adalah mahasiswa yang lulus dengan durasi yang sesuai. Semakin banyak mahasiswa yang lulus dengan durasi yang sesuai (dalam hal ini untuk sarjana yaitu <= 8 semester), harus berbanding yang sama atau lebih tinggi terhadap jumlah mahasiswa yang masuk pada suatu Universitas. Jika jumlah mahasiswa yang tidak lulus dengan durasi yang sesuai lebih tinggi, maka dapat menyebabkan lonjakan peningkatan jumlah data akademis dari semua mahasiswa yang masih terdaftar sehingga akan mempengaruhi citra dan reputasi dari Universitas yang nantinya dapat mengancam nilai akreditasi Universitas tersebut. Untuk mengatasi hal tersebut, maka diperlukan sistem yang dapat memprediksi kelulusan mahasiswa. Objek Penelitian ini dilakukan pada mahasiswa Universitas Dian Nuswantoro. Perancangan sistem prediksi menggunakan diagram Unified Modelling Language (UML). Diharapkan sistem prediksi kelulusan mahasiswa ini dapat berjalan optimal sehingga dapat memprediksi dan mengantisipasi secara dini profil kelulusan mahasiswa Universitas Dian Nuswantoro yang tidak sesuai meskipun di tengah wabah pandemi Covid-19.
Optimalisasi Model SciBERT dengan Attention-BiLSTM-CRF untuk Pengenalan Entitas Penyakit dalam Teks Biomedis Pamungkas, Tahta Arya; Salam, Abu
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7263

Abstract

This research aims to improve the performance of medical entity recognition in biomedical text by modifying the SciBERT model with Attention-BiLSTM-CRF. Although SciBERT, based on the BERT architecture and trained on biomedical text data, has proven effective in entity recognition, it still has limitations in handling complex medical entities, especially nested entities. As a solution, this research integrates Attention, BiLSTM, and CRF components into the SciBERT model to enhance entity recognition accuracy. Experimental results show that the SciBERT + Attention-BiLSTM-CRF model outperforms the SciBERT model across all key evaluation metrics. Precision improved by 1.7% (from 0.8221 to 0.8364), Recall increased by 2.9% (from 0.8537 to 0.8768), and F1-Score increased by 2.1% (from 0.8372 to 0.8554). These improvements demonstrate that this modification significantly enhances the model's ability to recognize more complex medical entities in biomedical text. The addition of Attention and BiLSTM enriches contextual understanding, while CRF ensures consistency across entity labels. These results indicate that this approach could significantly contribute to automated systems in processing medical data.
Pengembangan Chatbot Kesehatan Mental Berbasis Web Menggunakan Model Long Short-Term Memory (LSTM) Ardin, Akbar Ilham; Salam, Abu
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7282

Abstract

Mental health issues such as stress, anxiety, and academic burnout are increasingly prevalent among university students. However, many students remain reluctant or unable to access counseling services due to time limitations, social stigma, and a lack of available professionals. This study aims to develop CuraBot, a web-based chatbot designed to provide preliminary emotional support and mental health education in an instant, anonymous, and easily accessible manner for students. The system was developed using the Long Short-Term Memory (LSTM) algorithm, which is proven to be effective in understanding contextual text-based conversations. The dataset used consists of 1,624 conversational entries across 77 intent classes, adapted and localized from an open-source corpus to reflect the linguistic style and needs of Indonesian students. The development process involved several stages, including data preprocessing (lemmatization, tokenization, stopword removal, and padding), model training using TensorFlow, and deployment into a Flask-based web application. The model was evaluated using a separate test set of 244 entries, resulting in an accuracy of 89.9%, precision of 90.4%, recall of 89.1%, and an F1-score of 89.8%. These results indicate that the model can classify user intent with high accuracy. This research contributes to the development of a contextual, practical, and AI-based digital solution that supports early access to psychological services within university environments.
Enhancing Interpretable Multiclass Lung Cancer Severity Classification using TabNet Norman, Maria Bernadette Chayeenee; Dewi, Ika Novita; Salam, Abu; Utomo, Danang Wahyu; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11417

Abstract

Lung cancer poses a significant global mortality challenge, with early clinical detection hindered by non-specific symptoms making accurate diagnosis dependent on extracting subtle patterns from often complex medical tabular data. Traditional machine learning approaches often fall short in capturing intricate patterns within such heterogeneous datasets, hindering effective clinical decision support. This research introduces TabNet, an interpretable deep learning architecture, for multiclass lung cancer severity prediction (low, medium, high). Utilizing the Kaggle Lung Cancer dataset, our methodology leverages TabNet's unique attention-based feature selection for end-to-end processing of tabular data, enabling adaptive identification of key predictors and crucial model interpretability. To effectively assess its predictive capabilities and ensure robust performance, the model was trained with default configurations and validated through stratified 5-fold cross-validation, achieving outstanding performance on the test set: 98.50% accuracy, a 0.98 F1-score, and a 0.9996 macro-AUC-ROC. Beyond its robustness, confirmed by stable learning curves, interpretability analysis highlighted 'Genetic Risk' and 'Shortness of Breath' as dominant factors. Our results underscore TabNet's efficacy as a reliable, robust, and inherently interpretable solution, offering significant potential to improve the precision and transparency of lung cancer severity assessment in clinical practice.
Benchmarking Oversampling Strategies to Enhance the Performance of Machine Learning Algorithms in Hypertension Classification Maulia, Aenur Hakim; Salam, Abu
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11917

Abstract

This study benchmarks the effectiveness of three oversampling techniques, namely SMOTE, Random Oversampling (ROS), and ADASYN, in enhancing machine learning performance for multiclass hypertension classification. Using key physiological features and four optimized algorithms Logistic Regression, Support Vector Machine, Linear Discriminant Analysis, and Artificial Neural Networks, model performance was assessed using accuracy, F1-macro, and ROC AUC metrics. The experimental results indicate that the combination of SMOTE and Linear Discriminant Analysis (LDA) yields the highest overall performance, achieving an accuracy of 0.9773 and an F1-macro score of 0.9848. Logistic Regression demonstrates optimal results when paired with ROS, also reaching an accuracy of 0.9773. Artificial Neural Networks show the most substantial performance improvement under ADASYN, particularly reflected in higher F1-macro values. Although Support Vector Machine is less sensitive to oversampling interventions, it achieves a strong ROC AUC score of 0.9776 when trained using SMOTE. Overall, the findings confirm that oversampling techniques significantly improve classification performance in multilevel hypertension prediction, with SMOTE combined with LDA emerging as the most effective configuration.
Pelatihan Pembuatan Website Pembelajaran Berbasis Google Sites Bagi Siswa SMA Mardisiswa Semarang Utomo, Danang Wahyu; Kurniawan, Defri; Luthfiarta, Ardytha; Supriyanto, Catur; Winarsih, Nurul Anisa Sri; Fitriyani, Shelomita; Salam, Abu; Dewi, Ika Novita; Rakasiwi, Sindhu
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 7 No. 1 (2026): Edisi Januari - April
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v7i1.8211

Abstract

Perkembangan teknologi informasi memberikan dampak positif pada literasi digital, yaitu semakin berkembang. Adanya literasi digital menjadikan proses pembelajaran interaktif. Kompetensi TIK penting bagi siswa dalam mengembangkan media pembelajaran secara digital. Namun, SMA Mardisiswa menghadapi permasalahan rendahnya kompetensi TIK siswa, yang berdampak pada kurang optimalnya pemanfaatan media pembelajaran digital. Solusi yang diusulkan adalah pelatihan berbasis learning by doing dengan menerapkan siklus Kolb’s experiential learning yang menekankan praktik langsung dalam pembelajaran. Pelatihan dilaksanakan melalui tahapan pemberian materi, praktik pembuatan website menggunakan Google Sites, serta pendampingan. Peserta kegiatan berjumlah 30 siswa kelas XII. Hasil evaluasi menunjukkan adanya peningkatan kompetensi dasar pengembangan web pembelajaran. Rata-rata nilai post-test sebesar 84 meningkat dari nilai pre-test sebesar 64, atau mengalami peningkatan 31,25%. Selain itu, siswa mampu mengembangkan media pembelajaran berbasis web secara mandiri. Metode yang diterapkan terbukti dapat meningkatkan kompetensi TIK siswa dalam pengembangan web dasar.
Beyond Predictive Accuracy: Enhancing Parameter Stability in Multicollinear Time Series Forecasting via Regularisation Faisa, Daffa Kumara Khiar; Salam, Abu
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33925

Abstract

Multicollinearity in feature-based time series regression arises as a structural consequence of lagged and rolling feature construction. However, existing studies on Ridge and ElasticNet regularization adopt an accuracy-driven evaluation paradigm, with limited attention to parameter stability, shrinkage behavior, and sensitivity to regularization strength. This study shifts the evaluation of regularized linear models from predictive accuracy toward stability-oriented assessment. Using daily electricity consumption data from the UCI Repository, Linear Regression, Ridge, and ElasticNet models are examined under engineered temporal features derived from stability-based lag pruning, rolling statistics, and correlation-informed feature selection. Model evaluation focuses on bias–variance behavior, coefficient shrinkage, regularization sensitivity, and training–testing performance gaps. The results show that regularization improves stability, with the performance gap decreasing from 0.0961 in Linear Regression to 0.0608 under ElasticNet. These comparisons show that regularization stabilizes regression models via distinct shrinkage mechanisms, informing model selection beyond accuracy. Ridge exhibits conservative shrinkage averaging 6.06%, whereas ElasticNet induces stronger shrinkage averaging 46.32% and shows higher sensitivity to penalty strength. These findings provide methodological evidence that regularization in feature-based time series regression should be treated as a stability strategy rather than an accuracy optimization tool, offering guidance for electricity load forecasting under structurally redundant temporal features.
Optimalisasi Perilaku Hidup Bersih dan Sehat Melalui Aplikasi Kesehatan di SMP Ibu Kartini Egia Rosi Subhiyakto; Sindhu Rakasiwi; Ika Novita Dewi; Junta Zeniarja; Dhita Aulia Octaviani; Abu Salam; Shelomita Fitriyani; Almira Zuhrotus Safira
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3229

Abstract

Program Perilaku Hidup Bersih dan Sehat (PHBS) merupakan upaya penting dalam mendorong penerapan pola hidup sehat guna menjaga, merawat, serta meningkatkan derajat kesehatan. Penerapan gaya hidup sehat dapat mencegah berbagai penyakit yang berpotensi muncul di masyarakat. PHBS sangat tepat dikenalkan sejak usia sekolah, karena anak-anak termasuk kelompok yang rentan terhadap gangguan kesehatan akibat berbagai faktor. Perkembangan teknologi dalam bidang pendidikan telah terbukti mampu mengubah proses interaksi dan pembelajaran di kelas menjadi lebih efektif, efisien, mudah diakses, serta mendukung pengembangan keterampilan yang dibutuhkan di era digital, baik saat ini maupun di masa mendatang. Pemanfaatan aplikasi digital sebagai hasil perkembangan teknologi telah banyak diterapkan di bidang kesehatan dan pendidikan, yang keduanya saling berkaitan dan mendukung satu sama lain. Penyampaian informasi kesehatan membutuhkan peran pendidikan, sementara proses pendidikan juga tidak dapat berjalan optimal tanpa lingkungan yang sehat. Oleh karena itu, keberadaan teknologi dalam kedua bidang tersebut menjadi sangat krusial. Berdasarkan uraian tersebut, diperlukan pemberian pengetahuan mengenai PHBS kepada para siswa. Selain pemahaman secara teori, santri juga perlu mendapatkan pendampingan dalam penerapan PHBS secara langsung, serta dukungan teknologi berupa aplikasi digital agar proses pembelajaran menjadi lebih menarik dan efektif. Sebelum penerapan aplikasi tersebut, diperlukan sosialisasi dan pelatihan bagi pengasuh pondok pesantren terkait penggunaannya. Atas dasar pertimbangan tersebut, tim berinisiatif melaksanakan kegiatan Pengabdian Kepada Masyarakat dengan tema Pendampingan PHBS pada Siswa melalui Sosialisasi Aplikasi Digital yang berlokasi di SMP Ibu Kartini. Kegiatan ini diharapkan mampu membentuk kebiasaan PHBS dalam kehidupan sehari-hari santri serta mendorong mereka untuk menularkan perilaku positif tersebut kepada lingkungan sekitarnya.
Co-Authors Adhitya Nugraha Adhitya Nugraha Aini, Fajaria Nur Alan Ma’ruf, Farda Almira Zuhrotus Safira Alpiana, Vika Alzami, Farrikh Anisatawalanita Ukhifahdhina Ardin, Akbar Ilham Ardytha Luthfiarta Arifianti, Fidela Putri Arifin, Muhammad Farhan Astuti, Yani Parti Candra Irawan Catur Supriyanto Catur Supriyanto Catur Supriyanto Cinantya Paramita Damaswara, Silvester Aditya Debrina Luna Arghata Mangkawa Dhita Aulia Octaviani Dhita Aulia Octaviani Diana Aqmala Diki Retno Yuliani, Diki Retno Dimas Pratama, Yohanes Diyan Adiatma Dzaki, Azmi Abiyyu Egia Rosi Subhiyakto Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Erlin Dolphina Erwin Yudi Hidayat Erwin Yudi Hidayat Erwin Yudi Hidayat Etika Kartikadarma Fahmi Amiq Faisa, Daffa Kumara Khiar Farda Alan Ma&#039;ruf Ferry Bintang Nugroho Fitriyani, Shelomita Hapsari, Wanodya Haresta, Alif Agsakli Ifan Rizqa Ika Novita Dewi Iskandar, Deo Andrianto Juli Ratnawati Junta Zeniarja Kahingide, Hastyantoko Dwiki Khafiizh Hastuti Kurniawan, Defri Kurniawan, Wira Adi Kusmiyati Kusmiyati L. Budi Handoko Lesmarna, Salsabila Putri Maulana, Fadhli Faqih Maulia, Aenur Hakim Megantara, Rama Aria Moh. Sholik Muhammad Jamhari Mukti, David Ramantya Muljono Muljono Mulyanto, Edy Muzaki, Rizki Nuzul Nabila, Talitha Safa Nafanda, Cynthia Dwi Norman, Maria Bernadette Chayeenee Octaviani, Dhita Aulia Pamungkas, Tahta Arya Paramita, Cinantya Pawidya, Novandra Putra Prinantyo, Gilang Djati Putra, Aditya Herdiansyah Ramadhan Rakhmat Sani Ramadhan, Irfan Surya Restu Agung Pamuji Ricardus Anggi Pramunendar Riyan Ardiansyah Riza Amalia Shelomita Fitriyani Sidiq, Syaiful Rizal Sindhu Rakasiwi Sindhu Rakasiwi Utomo, Danang Wahyu Verdian Putra Wicaksana Wibowo, Isro' Rizky Winarsih, Nurul Anisa Sri Yani Parti Astuti Yonismara, Arvie Arvearie