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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

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Sosialisasi Computational Thingking Pada Guru MTs Yayasan NW Darul Abror Gunung Rajak Lombok Barat Hammad, Rifqi; Latif, Kurniadin Abd; Kartarina, Kartarina; Irfan, Pahrul; Syahrir, Moch.; Anas, Andi Sofyan; Cahyablindar, Ayu; Hidayatullah, M.
Jurnal Pengabdi Vol 4, No 1 (2021): April 2021
Publisher : Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jplp2km.v4i1.44516

Abstract

Computational thinking merupakan kemampuan intelektual yang digunakan dalam menyusun permasalahan serta solusinya, sehingga solusi yang diberikan dapat digunakan secara efektif oleh agen pemroses informasi baik itu manusia maupun komputer. Pengabdian ini dilakukan dengan tujuan untuk memperkenalkan konsep Computational thingking dan penerapannya di mata pelajaran. Sosialisasi dan pelatihan ini diberikan kepada guru-guru MTs Yayasan NW Darul Abror Gunung Rajak dengan jumlah peserta kurang lebih 52 orang. Pengabdian ini dilaksanakan menjadi 2 sesi yaitu sesi sosialisasi dan evaluasi. Pada bagian sosialisasi guru-guru diberikan materi tentang gambaran, manfaat dan pentingnya computational thinking untuk diterapkan sejak dini. Pada sesi sosialisasi ini juga para guru juga diberikan materi terkait penerapan computational thinking di berbagai macam mata pelajaran seperti matematika, IPA, IPS dan Bahasa. Pada sesi evaluasi, para guru diberikan beberapa pertanyaan untuk mengetahui sejauh mana para guru memahami konsep Computational thinking dan penerapannya dalam mata pelajaran. Berdasarkan hasil evaluasi, didapatkan bawah para guru sudah cukup memahami konsep Computational thinking dan penerapannya di mata pelajaran. Hal ini terlihat pada hasil evaluasi dari kegiatan yang telah dilakukan kepada para guru. Para guru mampu menjawab tentang computational thinking dan dapat membuat contoh penerapan computational thinking pada matapelajaran yang diajarkannya.
Deteksi dan Estimasi Kecepatan Kendaraan dalam Sistem Pengawasan Lalu Lintas Menggunakan Pengolahan Citra Muhammad Zulfikri; Hairani Hairani; Ahmad Ahmad; Kurniadin Abd. Latif; Rifqi Hammad; Moch. Syahrir
Techno.Com Vol 20, No 3 (2021): Agustus 2021
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v20i3.4588

Abstract

Deteksi objek berbasis pengolahan citra digital pada kendaraan sangat penting untuk diterapkan dalam membangun sistem pengawasan atau sebagai metode alternatif dalam mengumpulkan data statistik untuk pengambilan keputusan rekaya lalu lintas yang efisien. Pada penilitian ini, dibuat sistem deteksi kendaraan berbasis video lalu lintas untuk jenis kendaraan tertentu dengan menggunakan Haar Cascade Classifier dan estimasi kecepatan kendaraan dilakukan dengan menghitung perbedaan waktu pada Region of Interest (ROI) yang telah ditentukan dan hasilnya akan ditampilkan pada Radar Speed Design. Pengujian dilakukan dengan 5 video pengujian. Hasil yang didapatkan dari deteksi kendaraan yaitu nilai rata-rata recall 0.988 dan presisi 0.97 dan dari perhitungan kecepatan didapatkan nilai Mean Squared Error (MSE) yaitu 0,6.
Integrasi Barcode-QRCode pada Perpustakaan Universitas Bumigora Mataram dengan Konsep Sistem Terdistribusi Berbasis Mobile Moch. Syahrir; Muhammad Zulfikri; Muhamad Azwar
Jurnal Bumigora Information Technology (BITe) Vol 4 No 1 (2022)
Publisher : Prodi Ilmu Komputer Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v4i1.1902

Abstract

Perpustakaan di universitas bumigora telah menggunakan sistem untuk proses peminjaman dan pengembalian buku, akan tetapi masih terjadi pengantrian pada saat proses peminjaman dan pengembalian buku, karena harus mencari buku di rak-rak lalu diberikan ke kasir untuk di data dan lain sebagainnya. Pada umumnya mahasiswa universitas bumigora telah memiliki handphone ataupun smartphone android. Sementara di sisi lain semua buku-buku offline yang tersedia di perpustakkan universitas bumigora memiliki ISBN dengan barcode ataupun QRcode. Penggunaan barcode dan QRCode sudah sangat umum, tidak hanya pada barang-barang yang ada di toko-toko ataupun swalayan, akan tetapi penggunaan barcode dan QRCode sudah digunakan diberbagai bidang karena lebih praktis, dan tidak terkecuali ISBN buku-buku offline, oleh sebab itu sistem yang akan dibangun adalah sistem yang mampu menjawab permasalah yang ada di perpustakaan universitas bumigora dengan memanfaatkan kecanggihan teknologi dengan mengkolaborasikan sistem berbasis dekstop, mobile dan juga website sebagai back endnya untuk membangun program aplikasi perpustakaan yang mampu di olah oleh operator dengan baik dan pengguna bisa berinteraksi secara langsung. Adapun hasil dari quisioner likert yang digunakan untuk menguji sistem yang di bangun dengan 20 responden, mampu menghasilkan katergori sangat baik dengan nilai 97.70. Dengan sistem yang dibangun mampu memberikan solusi dan mempermudah bagian perpustakaan universitas bumigora dalam mengelola dan meningkatkan eksistensi perpustakaan universitas bumigora dalam melakukan pelayanan
Perancangan Aplikasi Pengolahan Data Kelompok Tani Ternak Provinsi NTB Muhamad Azwar; Syahrir Moch. Syahrir; Mudawil Mudawil Qulub
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol 2 No 1 (2022): Juni
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (346.462 KB) | DOI: 10.35746/bakwan.v2i1.210

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..
Pembuatan Portal Web SMKN 6 Mataram Sebagai Media Promosi dan Informasi Kelulusan Siswa Muhamad Azwar; Moch. Syahrir; Pahrul Irfan
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 | Full PDF (516.905 KB) | DOI: 10.35746/bakwan.v2i2.269

Abstract

A website is a collection of interconnected, publicly accessible Web pages that share a single domain name. Websites can be created and maintained by individuals, groups, businesses, or organizations to serve a variety of purposes, such as the rapiddelivery of information to the public. Websites come in almost endless variations, including educational sites, news sites, forums,social media sites, e-commerce sites, and so on. The pages on a website are usually a mixture of text and other media. There are no rules that determine the form of the website, one example is a website for schools which is one of the most effective mediafor delivering graduation information or information related to schools, especially at SMK Negeri 6 Mataram where students do notonly come from within the region but there are also those from outside the region so that this is one of the school's challenges to create an intermediary medium for delivering information nationally. Previously, SMK Negeri 6 Mataram in conveying graduation information to students still used paper and envelopes given to students as well as posting information on the wall magazine because it was too wasteful. and there are restrictions on the delivery of information. So that SMK Negeri 6 Mataramenforces thedelivery of graduation information by using a website so that it can be accessed by students in various places, so as to reduce the use of paper and envelopes, the website is built using a combination of codeigniter and Laravel frameworks in order to reduce hacking from irresponsible parties
Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems Moch. Syahrir; Lalu Zazuli Azhar Mardedi
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 13 No. 2 (2023): 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.
Analisis Perbandingan Algoritma Fp-Growth Dan Tpq-Apriori Dalam Menentukan Rule Based Terbaik Untuk Sistem Rekomendasi Produk Lalu Zazuli Azhar Mardedi Zazuli; Kartarina Kartarina; Moch. Syahrir Syahrir
Explore Vol 14 No 2 (2024): Juli 2024
Publisher : Universitas Teknologi Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35200/ex.v14i2.112

Abstract

The popular association rule algorithms are a priori and fp-growth, these two algorithms are very familiar among data mining researchers, however there are several weaknesses found in the association rule algorithm, including scanning the dataset for a long time in the process of searching for itemset frequencies, the use of large memory and the resulting base rules are sometimes less than optimal. In this research, the author compared the fp-growth and TPQ-apriori algorithms to analyze the base rule results of the two algorithms. TPQ-Apriori is an algorithm resulting from the development of the apriori algorithm, where the performance of the TPQ-Apriori algorithm is better than the traditional apriori algorithm in terms of the dataset scanning process in searching for itemset frequencies. For experiments, the fp-growth algorithm used the rapidminer tool while the TPQ-apriori algorithm used an application program that was built by ourselves. Meanwhile, the dataset used is sales data on CV. Charandita Kusuma NTB which has been uploaded to the Kaggle site. The base rules testing results are from the overall rule testing results with the CV sales dataset. Charandita Kusuma NTB can draw a conclusion that the larger the dataset to be processed, the more optimal the results will be if using the fp-growth rapidminer algorithm, but it is not optimal if the dataset to be processed is a small dataset. Some rules do not appear in the fp-growth algorithm with the rapidminer tool. Meanwhile, the TPQ-Apriori algorithm that has been developed is able to produce optimal rules for both large datasets and small datasets.
PENENTUAN POLA PEMBELIAN OBAT MENGGUNAKAN ALGORITMA APRIORI Syahrir, Moch; Rismayanti, Ria; Wicaksono, Moh Arief
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 11 No 2 (2021): September 2021
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (447.96 KB) | DOI: 10.33020/saintekom.v11i2.249

Abstract

Pharmacy is one of the businesses engaged in the health sector, especially product and services in improving public health. This service is carried out by a pharmacy manager in an effort to fulfill the duties and functions of pharmacy. This causes problems that occur in pharmacies, when search for drugs must be done by looking for one by one in transaction, because the pharmacy manager does not know sure the stock of drugs in the pharmacy. In addition, another problem is the arrangement of irregular drug layouts where the drug placement does not have a good standard layout. The method used in building this system is association with a priori on sales transaction data. then processed in a model to explore valuable information, as a policy standard in running a business. Application testing uses 3 minimum support and confidence values ​​so can get the rule and k-Itemset results from 3 different values.
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.
Face Recognition for Personal Data Collection using Eigenface, Support Vector Machine, and Viola Jones Method Mardedi, Lalu Zazuli Azhar; Zulfikri, Muhammad; Syahrir, Moch.; Latif, Kurniadin Abd.; Apriani, Apriani
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (2025): 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.v14i1.4728

Abstract

Personal data recording through facial recognition is a modern solution for individual identification; however, the main challenge lies in the accuracy and reliability of the system under various conditions. This study examines the implementation of machine learning as a solution, utilizing video and photo data for face detection and recognition. The study’s goal is to evaluate the effectiveness of facial image recognition by combining several methods, aiming for practical application across diverse settings, such as offices and schools. The methodology includes segmentation testing for edge detection, feature extraction, and real-time recognition. The system was developed using Eigenface, Support Vector Machine, and Viola-Jones methods, trained over 20 sessions. The results indicate that the system can recognize faces under both daytime and nighttime conditions, achieving 87% accuracy during the day and 81% at night. These findings make a significant contribution to the development of security systems based on facial recognition and emphasize the potential of this technology to enhance personal data security across various contexts
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