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All Journal Sistemasi: Jurnal Sistem Informasi Creative Information Technology Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING JCES (Journal of Character Education Society) JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH JISICOM (Journal of Information System, Infomatics and Computing) ICIT (Innovative Creative and Information Technology) Journal Jurnal Teknimedia: Teknologi Informasi dan Multimedia ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Pengabdian dan Inovasi
Mudawil Qulub
Universitas Bumigora, Mataram, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Nursing Social Sciences Other

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DETECTION OF CHILDREN'S NUTRITIONAL STATUS USING MACHINE LEARNING WITH LOGISTIC REGRESSION ALGORITHM Yuliana, Yuliana; Paradise, Paradise; Qulub, Mudawil
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 2 (2024): Maret 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i2.2973

Abstract

Abstract: Children's nutritional issues are an important concern for parents to pay attention to growth and development, especially health and well-being. According to the results of the Ministry of Health's Indonesian Nutrition Status Survey (SSGI), there are 4 nutritional problems for children in Indonesia, namely stunting, wasting, underweight and everweight. In this research, how to predict signs of symptoms of a decline in a child's nutritional status using a machine learning algorithm, a prediction model was designed using logistic regression in Python IDE to predict whether a child is indicated by a decline in nutrition or not. Dataset from Bengkayang Community Health Center data consisting of 657 pediatric patient data. The dataset is divided into 7 features (independent variables) and 1 predictor (dependent variable). Test results show perfect performance with precision, recall, F1-score, accuracy values of 100%. Then the visualization results on the ROC (Receiver Operating Characteristic) curve to depict the TP (True Positive) value on the Y axis against the FP (false Positive) value on the become overfit. It is recommended that in preparing the training dataset, measure the training data and reduce the features, after carrying out feature selection to increase the accuracy of the model.            Keywords: child nutritional status; growth and development logistic regression; machine learning Abstract: Masalah Gizi anak menjadi perhatian penting bagi orangtua untuk memperhatikan tumbuh kembang, terutama kesehatan dan kejahteraan. Menurut hasil survei status Gizi Indonesia (SSGI) Kemenkes memperlihatkan 4 permasalahan gizi anak di Indonesia yaitu stunting, wasting, underweight, dan everweight. Dalam penelitian ini, bagaimana memprediksi tanda gejala penurunan status gizi anak menggunakan  algoritma  machine  learning dirancang model prediksi menggunakan logistic regression pada Python IDE dengan  memprediksi anak  terindikasi  penurunan gizi  atau tidak. Dataset dari data Puskesmas Bengkayang  yang terdiri 657 data pasien anak. Dataset dibagi menjadi 7 feature (variabel independen) dan 1 predictor (variabel dependen). Hasil Pengujian memperlihatkan kinerja yang sempurna dengan nilai presisi, recall,  F1-score, akurasi, sebesar 100%. Kemudian hasil Visualisasi pada kurva ROC (Receiver Operating Characteristic) untuk menggambarkan nilai TP (True Positif) di sumbu Y terhadap nilai FP (false Positif) di sumbu X juga menunjukkan nilai yang sangat tinggi dan sudah mendekati angka 1 ini pertanda bahwa model ini menjadi overfit. Sebaiknya dalam persiapan training dataset diukur dengan data training dan mengurangi feature, setelah melakukan feature Selection untuk meningkatkan akurasi model. Keywords: logistic regression; machine learning; status gizi anak; tumbuh kembang
Peningkatan Kompetensi Guru SMAN 7 Mataram dalam Melaksanakan Pembelajaran dengan Pendekatan Deep Learning Azwar, Muhamad; Hariyadi, I Putu; Azhar, Raisul; Priyanto, Dadang; Adil, Ahmat; Santoso, Heroe; Syahrir, Moch.; Augustin, Kartarina; Zulkipli, Zulkipli; Darma, I Made Yadi; Asroni, Ondi; Qulub, Mudawil; Azhar, Lalu Zazuli; Widyawati, Lilik; Anas, Andi Sofyan
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol. 5 No. 2 (2025): Desember
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/bakwan.v5i2.852

Abstract

The capability of educators to respond to the dynamics of 21st-century education is a primary determinant in establishing a high-quality learning environment. Based on initial findings at SMAN 7 Mataram, a disparity was identified between the urgency of applying varied learning models and the reality in the field, which still relies heavily on conventional, teacher-centered approaches. This situation implies minimal active student participation and suboptimal stimulation of critical thinking skills or Higher Order Thinking Skills (HOTS). This community service program was initiated to escalate teacher capacity at SMAN 7 Mataram, specifically in designing Deep Learning-based schemes. The implementation approach adopted the Participatory Action Research (PAR) method, involving the full attention of 70 teachers through a series of phases, ranging from preparation and implementation to evaluation and mentoring. Key interventions included training on compiling Deep Learning-oriented Lesson Plans and teaching simulations. Program effectiveness was measured through questionnaires, lesson plan document reviews, and observations. Evaluation data showed a substantial positive impact, marked by an increase in conceptual understanding of Deep Learning indicators (40%), 6C principles (40%), the teacher's function as a facilitator (32%), and the application of authentic assessment (40%). In terms of implementation, the quality of lesson plans accommodating student-centered activities surged significantly from 30% in the pre-activity phase to 100% after the activity. It can be concluded that this program effectively boosts teachers' pedagogical competence comprehensively and encourages the transformation of teaching practices in the classroom to become more dynamic.
Implementasi Teknik Normalisasi Basis Data untuk Menghindari Redundansi Data pada Sistem Penjualan Suriyati Suriyati; Heroe Santoso; Muhamad Azwar; Miftahul Madani; Ismarmiaty Ismarmiaty; Muhammad Innuddin; Mudawil Qulub; I Made Yadi Dharma; Muhamad Wisnu Alfiansyah
Journal of Information System, Informatics and Computing Vol 10 No 1 (2026): JISICOM (June 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v10i1.2354

Abstract

In today's digital era, efficient information management is key to a business's operational success. In the sales sector, high transaction volumes generate vast amounts of data daily. However, many sales systems still face a classic data architecture problem: data redundancy, or unnecessary duplication of information. Data redundancy is not simply a matter of inefficient storage; it directly impacts the emergence of data anomalies. When the same data is stored in multiple locations, the risk of information inconsistency during updates, deletions, and insertions is very high. Inconsistent data can mislead management in decision-making, for example, errors in inventory reports or financial recapitulations. The main objective of this research is to eliminate data redundancy and prevent anomalies in the sales system to ensure information integrity. Through systematic table organization, this research seeks to create a more efficient and accurate database architecture to support management decision-making. The method used is structured analysis through data decomposition stages. The process begins with collecting raw (unnormalized) transaction data, which is then processed through the 1NF, 2NF, and 3NF stages. The final stage involves validating the Create, Read, Update, and Delete (CRUD) functions to ensure the consistency of the new schema. The desired result is an optimal relational database schema, where data is logically distributed into specific, interconnected tables. This implementation will result in a lighter system, efficient storage usage, and error-free sales reporting.
Automation of Water Quality Recovery in Vannamei Shrimp Aquaculture using the KNN Algorithm and Fuzzy Logic Mudawil Qulub; Muh Fahruddin; Muhamad Sari Rizki; Adi Saputra
SISTEMASI Vol 15, No 3 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i3.5888

Abstract

Vannamei shrimp play a crucial role in Indonesia’s fisheries and export industries. Despite their high potential, shrimp aquaculture still faces significant challenges, particularly disease susceptibility caused by fluctuations in water quality and pond environmental conditions. This study aims to develop a system capable of automatically monitoring and restoring water quality using the K-Nearest Neighbors (KNN) algorithm and fuzzy logic. The research adopts a research and development (R&D) approach, which includes problem analysis, data collection, system design, development, testing, evaluation, and implementation. The system employs the KNN algorithm with K=5K = 5K=5 to diagnose water quality conditions, while fuzzy logic is used to automatically control aerators, pumps, and drainage systems. The sensors utilized include salinity, pH, temperature, dissolved oxygen, and turbidity, all integrated through an ESP32 microcontroller within an Internet of Things (IoT) network. The results demonstrate that the system achieves a diagnostic accuracy of 95% and is capable of automatically controlling recovery devices. With real-time and automated operation, the system effectively maintains pond water quality, thereby improving productivity and the overall success of vannamei shrimp aquaculture.
Pelatihan dan Pendampingan Digital Marketing untuk Meningkatkan Daya Saing UMKM Binaan BRIDA Provinsi Nusa Tenggara Barat Bukran Bukran; Muhammad Tahir; Muhamad Wisnu Alfiansyah; Miftahul Madani; Mudawil Qulub; Hairani Hairani
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 7 No. 1 (2026): ADMA: Jurnal Pengabdian dan Pemberdayaan Mayarakat
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v7i1.6503

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan kapasitas pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) dalam memanfaatkan digital marketing sebagai strategi peningkatan daya saing usaha di era ekonomi digital. Mitra kegiatan adalah pelaku UMKM binaan Badan Riset dan Inovasi Daerah (BRIDA) Provinsi Nusa Tenggara Barat. Permasalahan yang dihadapi mitra meliputi rendahnya literasi digital, keterbatasan pemanfaatan media sosial sebagai media promosi, serta belum optimalnya penggunaan marketplace dan platform digital untuk pemasaran produk. Metode pelaksanaan dilakukan melalui sosialisasi, pelatihan, praktik langsung, pendampingan, dan evaluasi. Materi yang diberikan meliputi penyusunan strategi pemasaran digital, pembuatan konten promosi menggunakan aplikasi Canva, optimalisasi media sosial, pemanfaatan marketplace, serta pengenalan Google Business Profile. Hasil kegiatan menunjukkan adanya peningkatan pemahaman peserta mengenai strategi pemasaran digital serta meningkatnya kemampuan peserta dalam membuat konten promosi dan memasarkan produk secara digital. Peserta juga menunjukkan antusiasme tinggi selama sesi praktik dan diskusi. Kegiatan ini diharapkan mampu mendorong transformasi digital UMKM sehingga mampu meningkatkan daya saing serta memperluas jangkauan pasar.
IMPLEMENTASI ALGORITMA DEPTH-FIRST SEARCH DAN BREADTH-FIRST SEARCH PADA DOKUMEN AKREDITASI Yuliana Yuliana; Noviyanti Noviyanti; Mudawil Qulub
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 7 No. 1 (2024): February 2024
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v7i1.1733

Abstract

Sistem arsip dokumen dapat digunakan sebagai media penyimpanan data untuk memudahkan persiapan proses asesmen lapangan untuk akreditasi. Mekanisme pencarian data merupakan bagian penting dalam arsip digital. Dengan menggunakan Teknologi Kecerdasan Buatan dalam teknik pencarian yaitu depth-first search dan breadth-first search. Kedua metode ini dipadukan untuk menyelesaikan permasalahan yang tentunya mempunyai kelebihan dan kekurangan. Sistem dokumen digital dapat melakukan proses pencarian, penyampaian, pemantauan dan pengambilan data. Dalam proses pengujian dan mekanisme analisa pencarian data, sistem menerapkan output teknik penggabungan/kolaborasi dari depth-first search dan breadth-first search yang diakhiri melalui penemuan mendalam ke dalam database untuk menyesuaikan parameter hingga ditentukan query untuk mengeksekusi hasil keluaran parameter dan kemudian umpan balik diberikan kembali ke sistem. Topik penelitiannya ini menemukan jalur cepat akreditasi penyimpanan dokumen arsip dengan teknik penggabungan algoritma yang bisa digunakan dalam menemukan rute cepat pada saat menemukan tujuan tertentu. Rekomendasi pada penelitian yang ingin mendalami topik yang sama adalah dengan menggabungkan tambahan algoritma lain, yaitu teknik blind search pada Artificial Intelligence.
Co-Authors Adi Saputra Ahmat Adil Alfia, Kharunia Andi Sofyan Anas Andi Sunyoto Augustin, Kartarina Azhar, Lalu Zazuli Azhar, Raisul Budiarta, Meiza Ariya Bukran Bukran Darma, I Made Yadi Eka Nurul Qomaliyah Ema Utami Fatimatuzzahra Fatimatuzzahra Hairani Hairani Hammad, Rifqi Hariyadi, I Putu Heroe Santoso I Made Yadi Dharma Ismarmiaty Ismarmiaty Kresnapati, I Nyoman Bagus Aji Lilik Widyawati Miftahul Madani Muh. Fahruddin Muhamad Azwar Muhamad Azwar Muhamad Azwar, Muhamad Muhamad Sari Rizki Muhamad Wisnu Alfiansyah Muhamad Wisnu Alfiansyah Muhammad Eka Putra Ramandha Muhammad Innuddin Muhammad Tahir Nisrina Yulia Setyowati Noviyanti. P Nurul Indriani Ondi Asroni Pahrul Irfan Paradise, Paradise Ramadhani, Muhammad Wisda Pradja Santoso, Heroe Suriyati Suriyati Syahrir, Moch. Vera Wati Widyati, Baiq Dinna Yuliana Yuliana Yuliana Yuliana Yuliana Zahrah, Zahrah Zulkipli Zulkipli