Article Per Year (5 Year)
p-Index From 2021 - 2026
3.706
P-Index
This Author published in this journals
All Journal Jurnal Sains dan Teknologi Techno.Com: Jurnal Teknologi Informasi Sistemasi: Jurnal Sistem Informasi JURNAL PENGABDI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Multimedia Jurnal Bumigora Information Technology (BITe) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Minfo Polgan (JMP) MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) INCOME: Indonesian Journal of Community Service and Engagement Bakti Sekawan : Jurnal Pengabdian Masyarakat Explore Prosiding Seminar Nasional CORISINDO
Syahrir, Moch.
Universitas Bumigora
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

Published : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 23 Documents
Search

 1   2   3 
Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems Syahrir, Moch.; Mardedi, Lalu Zazuli Azhar
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 13 No. 2 (2023): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v13i2.52-67

Abstract

The popular association rule algorithms are Apriori and fp-growth; both of these algorithms are very familiar among data mining researchers; however, there are some weaknesses found in the association rule algorithm, including long dataset scans in the process of finding the frequency of the item set, using large memory, and the resulting rules being sometimes less than optimal. In this study, the authors made a comparison of the fp-growth, Apriori, and TPQ-Apriori algorithms to analyze the rule results of the three algorithms. TPQ- Apriori is an algorithm developed from the Apriori algorithm. For experiments, the Apriori and fp-growth algorithms use RapidMiner and Weka tools, while the TPQ-apriori algorithm uses self-built application programs. The dataset used is the sales data for the Kopegtel NTB department store, which has been uploaded on the Kaggle site. As for the results of testing the base rules from the overall results of testing the rules with the good Kopegtel dataset for 100%, 50%, and 25% of the total volume of the dataset, a conclusion can be drawn that the larger the dataset to be processed, the results will be more optimal when using the fp-growth algorithm RapidMiner, but not optimal if the dataset to be processed is small. It is different from using the Apriori and Weka FP-growth algorithms, where the resulting rules are less than optimal if the dataset used is large and optimal if the dataset is small. Several rules do not appear in the fp-growth and Apriori Weka algorithms because the two algorithms do not have a tolerance value in Weka's tools for the support of the rules that will be displayed. Meanwhile, the TPQ- Apriori algorithm that has been developed is capable of producing optimal rules for both large datasets and small datasets.
PERANCANGAN APLIKASI PENGOLAHAN DATA KELOMPOK TANI TERNAK PROVINSI NTB Azwar, Muhamad; Moch. Syahrir; Mudawil Qulub
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol. 2 No. 2 (2022): Desember
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/bakwan.v2i2.209

Abstract

The Department of Animal Husbandry and Animal Health is one of the government agencies of West Nusa Tenggara Province which has the task of improving the welfare of the community and reducing poverty in West Nusa Tenggara Province by providing grants in the form of livestock assistance or other assistance, assistance is given to livestock farmer groups in all regencies/cities throughout the province. -NTB has criteria that have been determined by the Animal Husbandry and Animal Health Service of the Province of NTB, the assistance provided is sourced from the Provincial Revenue and Expenditure Budget (APBD) and the State Revenue and Expenditure Budget (APBN), because the process of providing assistance through the selection of submitted proposals manually by livestock farmer groups with three processes that become important points, namely recapitulation, validation and verification which takes a long time and the lack of information related to the development of livestock farmer groups in all regions of NTB that have received assistance, therefore an application or application is needed. u an information system that can assist the Department of Animal Husbandry and Animal Health of the Province of NTB in processing livestock farmer group data, ranking as a good decision maker..
Perbandingan Metode Berbasis Decision Tree dalam Deteksi Penyakit Paru-Paru Kurniawati, Lely; Priyanto, Dadang; Ningsih, Neny Sulistia; Syahrir, Moch; Rismayati, Ria
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i1.4909

Abstract

Background: Lung disease is a leading cause of death globally, with more than 4 million cases each year, including 500,000 new cases in Indonesia, most of which are detected at an advanced stage.Objective: This study aims to compare the performance of three decision tree algorithms, XGBoost, C4.5, and Random Forest, in detecting lung disease and to determine the best method based on evaluation metrics.Methods: A total of 30,000 data samples from Kaggle were processed through a cleaning stage using the IQR method, categorical attribute coding, and data division into 80% for training and 20% for testing. The classification models used include XGBoost, C4.5, and Random Forest. Model performance evaluation used a confusion matrix, accuracy, precision, recall, and F1-score.Result: The results showed that the C4.5 algorithm had the best performance with an accuracy of 94.33% and zero false negatives. XGBoost followed with an accuracy of 93.18%, while Random Forest was the lowest (90.07%).Conclusion: These findings indicate that C4.5 has great potential in an accurate early detection system, helping to reduce the risk of misdiagnosis, especially in false negative cases, and supporting clinical decision making in health facilities. 
Perbandingan Algoritma Sarima dan Prophet Untuk Peramalan Trend Penjualan Voucher Game Online Rizki, M; Priyanto, Dadang; Martono, Galih Hendro; Sulistianingsih, Neny; Syahrir, Moch
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15083

Abstract

Industri game online terus mengalami perkembangan pesat, mendorong kebutuhan akan sistem peramalan yang akurat untuk mendukung pengambilan keputusan strategis dalam manajemen penjualan dan promosi. Studi ini bertujuan untuk membandingkan kinerja dua algoritma peramalan deret waktu, yaitu Seasonal Autoregressive Integrated Moving Average (SARIMA) dan Prophet, dalam memprediksi tren penjualan voucher game online di platform Kiyystore. Data yang digunakan dalam penelitian ini mencakup transaksi historis dari tahun 2022 hingga 2024, dengan total 5,530 data penjualan. Studi ini menerapkan metodologi Cross Industry Standard Process for Data Mining (CRISP DM) yang terdiri dari tahap pemahaman bisnis, pemrosesan data, pemodelan, dan evaluasi. Model SARIMA dipilih karena kemampuannya untuk menangkap pola musiman dan tren dalam data stasioner. Sementara itu, Prophet digunakan karena dirancang untuk menangani tren non-linear, pola musiman, dan anomali secara otomatis. Evaluasi kinerja dari kedua algoritma dilakukan menggunakan dua metrik utama, yaitu Mean Absolute Error (MAE) dan Root Mean Squared Error (RMSE). Hasil penelitian menunjukkan bahwa Prophet unggul dalam metrik MAE dengan nilai 0,7054, yang menunjukkan kinerja yang lebih baik dalam meminimalkan kesalahan rata-rata. Di sisi lain, SARIMA menunjukkan keunggulan dalam metrik RMSE dengan nilai 0,9514, yang berarti model ini lebih efektif dalam menangani kesalahan besar atau pencilan dalam prediksi. Studi ini memberikan kontribusi penting dalam pemilihan metode peramalan yang sesuai dengan karakteristik data. Dengan memahami keunggulan masing-masing algoritma, pelaku industri game online dapat lebih optimal dalam merencanakan strategi stok dan promosi, sehingga meningkatkan efisiensi dan daya saing bisnis secara keseluruhan
Integrasi Bagging dan Stacking Untuk Memperbaiki Kinerja Algoritma Klasifikasi C4.5 dan K-Nearest Neighbor(KNN) Syahrir, Moch.; Switrayana, I Nyoman; Darmawan, I Made Angga Wahyu
JST (Jurnal Sains dan Teknologi) Vol. 14 No. 2 (2025): July
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jst-undiksha.v14i2.100794

Abstract

Permasalahan utama dalam klasifikasi data berdimensi tinggi adalah lambatnya proses pemindaian dan inkonsistensi akurasi model, yang berdampak negatif terhadap kualitas informasi dan pengambilan keputusan berbasis data. Dalam konteks prediksi risiko keuangan, seperti kredit macet, keterbatasan ini dapat menghambat efektivitas sistem pendukung keputusan. Penelitian ini bertujuan untuk mengevaluasi dan mengembangkan kinerja algoritma klasifikasi dasar, yaitu C4.5 dan K-Nearest Neighbor (KNN), melalui integrasi teknik ensemble learning bagging dan stacking. Penelitian ini merupakan penelitian kuantitatif dengan desain eksperimen komparatif. Subjek penelitian adalah empat dataset publik yang merepresentasikan data keuangan, yaitu Bank Marketing (41188 record), Credit Card (1319 record), Credit Risk Assessment (32581 record), dan Credit Card Defaulter (10000 record). Data dikumpulkan dari repositori Kaggle, kemudian diolah menggunakan algoritma C4.5 dan KNN yang diintegrasikan dengan teknik ensemble. Instrumen penelitian berupa implementasi model klasifikasi menggunakan perangkat lunak Rapid Miner dan Python, dengan pengujian validitas melalui k-fold cross validation dan pengukuran reliabilitas menggunakan metrik akurasi. Teknik analisis data meliputi pengujian performa model berdasarkan nilai akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa bagging dengan algoritma C4.5 memberikan hasil terbaik pada tiga dari empat dataset, masing-masing dengan akurasi 91,21%, 97,73%, dan 92,11%. Sedangkan pada dataset keempat, kombinasi bagging dan KNN menghasilkan akurasi tertinggi sebesar 97,06%. Simpulan dari penelitian ini adalah bahwa teknik bagging secara signifikan mampu meningkatkan akurasi dan konsistensi model klasifikasi dasar. Implikasi dari hasil ini menunjukkan bahwa integrasi metode ensemble dapat menjadi solusi praktis dan teoretis untuk meningkatkan kualitas klasifikasi dalam domain keuangan, khususnya dalam memprediksi risiko kredit.
Using a Partition System to Improve the Performance of the Apriori Algorithm in Speeding Up Itemset Frequency Search Process Syahrir, Moch; Hammad, Rifqi; Abd. Latif, Kurniadin; Rosanensi, Melati
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3610

Abstract

The apriori algorithm uses minimum support and minimum confidence to determine appropriate itemset rules for decision making. The problem faced in this research is how to improve the performance of the a priori algorithm in the process of searching for itemset frequencies using data partition techniques, and be able to produce optimal and consistent rules. To overcome this problem, the author implemented the a priori method and partition system to improve the performance of the a priori algorithm for the itemset frequency search process by taking public data in the form of supermarket transaction data. In this research, the performance of the a priori algorithm was tested with and without a partition system. The data used in this research consists of 350 transaction data from 1784 records with a 4-itemset pattern, minimum support value of 20% and minimum confidence of 0.5 with the best standard rules for determining minimum confidence of 0.8. Based on this research carried out, the research results obtained are that for comparison of time and memory usage the apriori algorithm with a partition system is much faster than the apriori algorithm without a partition system, while memory usage is relatively less for the apriori algorithm with the system than the apriori algorithm without a partition system.
Enhancing Mental Illness Predictions: Analyzing Trends Using Multiple Linear Regression and Neural Network Backpropagation Riosatria, Riosatria; Hairani, Hairani; Anggrawan, Anthony; Syahrir, Moch.
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4391

Abstract

The increasing number of mental health cases caused by various factors such as social changes, economic pressures, and technological advancements has made it difficult to accurately predict the number of cases, hindering prevention and early intervention efforts. Therefore, developing more accurate, data-driven predictive models is necessary to improve the effectiveness of prevention and intervention. This study aims to develop a predictive model for the number of mental health cases using Multiple Linear Regression and Neural Network Backpropagation methods. The study employs two predictive methods, Multiple Linear Regression and Neural Network Backpropagation to forecast future trends in the number of mental health cases. The findings reveal that the Neural Network Backpropagation method provides more accurate predictions than Multiple Linear Regression in forecasting mental health case trends. Specifically, the Neural Network Backpropagation method resulted in an MAE of 111.39 and a MAPE of 1.77%, while the Multiple Linear Regression method produced an MAE of 115.24 and a MAPE of 1.83%. Thus, the implication of this study is that the Neural Network Backpropagation method can be utilized to predict trends in the number of mental health cases due to its ability to provide highly accurate predictions.
Pemantauan Vibrasi dan Temperatur Motor Listrik Menggunakan Piezo, NTC Sensor, IoT ESP32 dan Analisa Regresi Guna Penentuan Waktu Tindakan Pemeliharaan Widayat, Windy; Syahrir, Moch; Zulfikri, Muhammad
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5518

Abstract

Motor listrik merupakan komponen vital dalam sistem pembangkitan dan perindustrian.  Kerusakan umum pada bearing dan kumparan dapat menyebabkan biaya tinggi dan waktu henti produksi.  Penelitian ini merancang alat pemantauan vibrasi dan temperatur motor listrik berbasis Internet of Things  (IoT) menggunakan sensor piezoelektrik dan thermistor NTC dengan mikrokontroler ESP32 serta  komunikasi data berbasis MQTT yang dapat dimonitor secara real-time melalui antarmuka web. Data  vibrasi kemudian dianalisis menggunakan regresi untuk memprediksi batas ambang dan waktu ideal  tindakan pemeliharaan. Hasil pengujian menunjukkan deviasi maksimum sensor piezo sebesar 10%  dibandingkan alat industri (VibExpert), dan sensor NTC memiliki deviasi maksimum ±0,9°C terhadap  termometer inframerah (FLIR). Analisis regresi memberikan nilai koefisien determinasi (R²) yang cukup  signifikan pada tiga sumbu getaran, yang digunakan untuk menentukan waktu prediksi tindakan  pemeliharaan. Sistem ini terbukti efektif sebagai alat bantu prediktif dalam pemeliharaan motor listrik.
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.
Optimalisasi Pemanfaatan Platform Digital dalam Merancang Identitas Produk dan Pemasaran Digital Madu Trigona Lebah Prawira Desa Sokong Lombok Utara Tri Sujaka, Tomi; Abd Latif, Kurniadin; Syahrir, Moch; Hammad, Rifqi; Bukran
INCOME: Indonesian Journal of Community Service and Engagement Vol 4 No 4 (2025)
Publisher : EDUPEDIA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56855/income.v4i4.1907

Abstract

Kelompok Ternak Madu Trigona Lebah Prawira merupakan kelompok ternak yang ada pada wilayah Desa Sokong Kecamatan Tanjung Kabupaten Lombok Utara. Permasalahan utama yang dihadapi mitra meliputi rendahnya pemanfaatan media digital, belum terbentuknya identitas merek yang kuat, serta keterbatasan pengetahuan dan keterampilan dalam pemasaran digital. Kegiatan ini bertujuan untuk mengoptimalkan pemanfaatan platform digital dalam mendukung branding dan pemasaran produk madu trigona. Metode Pelaksanaan kegiatan meliputi sosialisasi, pelatihan digital branding dan pemasaran digital, penerapan solusi pemasaran digital, dan pendampingan serta evaluasi. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan mitra dalam membangun identitas merek, menghasilkan konten promosi yang lebih menarik dan konsisten, serta memanfaatkan media sosial dan marketplace untuk memperluas jangkauan pemasaran. Pembahasan menunjukkan bahwa penerapan strategi branding dan pemasaran digital mampu meningkatkan visibilitas produk, interaksi dengan konsumen, serta potensi peningkatan nilai jual madu trigona. Optimalisasi pemanfaatan platform digital melalui pendekatan pelatihan dan pendampingan terbukti efektif dalam meningkatkan daya saing dan keberlanjutan usaha madu trigona di era digital.
Co-Authors Ahmad Ahmad Ahmat Adil Andi Sofyan Anas Anthony Anggrawan Apriani Apriani Augustin, Kartarina Ayu Dasriani, Ni Gusti Azhar, Lalu Zazuli Azhar, Raisul Bukran Cahyablindar, Ayu Darma, I Made Yadi Darmawan, I Made Angga Wahyu Fatimatuzzahra Fatimatuzzahra Galih Hendro Martono Hairani Hairani Hammad, Rifqi Hariyadi, I Putu I Nyoman Switrayana Kartarina, Kartarina Kurniadin Abd Latif Lely Kurniawati Lilik Widyawati M. Hidayatullah Mardedi, Lalu Zazuli Azhar Mardedi, Lalu Zazuli Azhar Melati Rosanensi Mudawil Qulub Muhamad Azwar Muhamad Azwar, Muhamad Muhammad Azwar Muhammad Zulfikri Muhammad Zulfikri Neny Sulistianingsih Ningsih, Neny Sulistia Ondi Asroni Pahrul Irfan Qulub, Mudawil Ria Rismayati Rifqi Hammad Rijaldi, Rian Maulana Riosatria, Riosatria Rismayanti, Ria Rizki, M Santoso, Heroe Sirojul Hadi Tri Sujaka, Tomi Wicaksono, Moh Arief Widayat, Windy Zulfikri, Muhammad Zulkipli Zulkipli