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All Journal TEKNIK INFORMATIKA JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Mantik Penusa MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Varian Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Abdi Masyarakat Indonesia Indo-MathEdu Intellectuals Journal Abdi Widya: Jurnal Pengabdian Masyarakat Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Jurnal Ilmiah Pengabdian dan Inovasi INTAN CENDEKIA: JURNAL PENGABDIAN MASYARAKAT EMPOWERMENT: Journal of Community Practice
Anthony Anggrawan
Ilmu Komputer, Fakultas Teknik, Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Komparasi Ekstraksi Fitur dalam Klasifikasi Teks Multilabel Menggunakan Algoritma Machine Learning Lusiana Efrizoni; Sarjon Defit; Muhammad Tajuddin; Anthony Anggrawan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 21 No 3 (2022)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (939.233 KB) | DOI: 10.30812/matrik.v21i3.1851

Abstract

Ektraksi fitur dan algoritma klasifikasi teks merupakan bagian penting dari pekerjaan klasifikasi teks, yang memiliki dampak langsung pada efek klasifikasi teks. Algoritma machine learning tradisional seperti Na¨ıve Bayes, Support Vector Machines, Decision Tree, K-Nearest Neighbors, Random Forest, Logistic Regression telah berhasil dalam melakukan klasifikasi teks dengan ektraksi fitur i.e. Bag ofWord (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Documents to Vector (Doc2Vec), Word to Vector (word2Vec). Namun, bagaimana menggunakan vektor kata untuk merepresentasikan teks pada klasifikasi teks menggunakan algoritma machine learning dengan lebih baik selalumenjadi poin yang sulit dalam pekerjaan Natural Language Processing saat ini. Makalah ini bertujuan untuk membandingkan kinerja dari ekstraksi fitur seperti BoW, TF-IDF, Doc2Vec dan Word2Vec dalam melakukan klasifikasi teks dengan menggunakan algoritma machine learning. Dataset yang digunakan sebanyak 1000 sample yang berasal dari tribunnews.com dengan split data 50:50, 70:30, 80:20 dan 90:10. Hasil dari percobaan menunjukkan bahwa algoritma Na¨ıve Bayes memiliki akurasi tertinggi dengan menggunakan ekstraksi fitur TF-IDF sebesar 87% dan BoW sebesar 83%. Untuk ekstraksi fitur Doc2Vec, akurasi tertinggi pada algoritma SVM sebesar 81%. Sedangkan ekstraksi fitur Word2Vec dengan algoritma machine learning (i.e. i.e. Na¨ıve Bayes, Support Vector Machines, Decision Tree, K-Nearest Neighbors, Random Forest, Logistic Regression) memiliki akurasi model dibawah 50%. Hal ini menyatakan, bahwa Word2Vec kurang optimal digunakan bersama algoritma machine learning, khususnya pada dataset tribunnews.com.
PELATIHAN DESAIN KEMASAN DI SEKOLAH MENENGAH KEJURUAN NEGERI 1 SIKUR Hasbullah; Anthony Anggrawan; I Nyoman Yoga Sumadewa; Christofer Satria; Baiq Fitria Rahmiati
Abdi Widya: Jurnal Pengabdian Masyarakat Vol 1 No 2 (2022): Abdi Widya: Jurnal Pengabdian Masyarakat
Publisher : UPT Pusat Penerbitan LP2MPP ISI Denpasar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (349.12 KB)

Abstract

Sekolah Menengah Kejuruan Negeri 1 Sikur sebagai sekolah keunggulan bidang seni dan industri kreatif yang beroreintasi pada pariwisata. Kebutuhan pariwisata tentu membutuhkan tampilan branding yang menarik salah satunya adalah kemasan. Namun, dengan kurangnya tenanga pengajar yang memiliki keahlian dalam bidang desain kemasan, maka Sebagian besar peserta didik di sekolah tersebut masih kurang memahami komponen-komponen desain kemasan. Tujuan diadakannya pelatihan ini sebagai wahana berbagi dan membuka wawasan tentang desain kemasan bagi peserta didik di Sekolah Menengah Kejuruan Negeri 1 Sikur. Metode yang digunakan dalam kegiatan ini adalah Latihan terbimbing. Melalui proses simulasi dan latihan yang didampingi oleh tim pengabdi maka dapat menghasilkan desain kemasan makanan yang bervaiasi. Kegiatan ini dimulai dari mendesain menggunakan aplikasi grafis di computer sampai dengan membuat standing pouch food Oleh karena itu, kegiatan pelatihan ini perlu dilakukan pada tingkatan sekolah agar mendapatkan ilmu tambahan dan kreativitas.
Penerapan Strategi Pemasaran dalam Meningkatkan Omset Penjualan Jajanan Lokal UMKM di Kota Mataram Rini Anggriani; Anthony Anggrawan; Gusti Ayu Dasriani; Raden Bagus Faizal Irani Sidharta; Dafa Awanta; Jean Suciasti Gunawan
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 3 No 1 (2022): ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v3i1.2120

Abstract

The Micro, Small and Medium Enterprises (MSME) sector is one of the important sectors as the main support for the community's economy.However, the Covid-19 event had a significant impact on this sector.Indeed, Covid-19 has subsided, but the impact of this phenomenon is still affecting all sectors, one of which is the Micro, Small and Medium Enterprises (MSME) sector. This service program is carried out on local snack products in the city of Mataram with the target of helping increase sales turnover of The Micro, Small and Medium Enterprises (MSME) products through optimizing the application of mix marketing strategies. The method used is a qualitative descriptive approach with data processing techniques using triangulation.The results of this service show that the application of the marketing mix marketing strategy that is carried out can have a positive and effective impact on increasing the sales turnover of The Micro, Small and Medium Enterprises (MSME) products in the city of Mataram.
Pendampingan Fotografi dan Desain Grafis di Sekolah Menengah Pertama Katolik (SMPK Kesuma) Mataram Hasbullah Hasbullah; Anthony Anggrawan; Christofer Satria; I Nyoman Yoga Sumadewa; Baiq Fitria Rahmiati
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 3 No 1 (2022): ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v3i1.2135

Abstract

Condition of the Catholic Junior High School (SMPK Kesuma) in the COVID-19 pandemic has paralyzed the field trip activities programmed by the school every year. With the soaring Covid-19 cases in Mataram, the school replaced the field trip activities with photography and graphic design training. Photography and graphic design training aims to foster students' talents and interests in dealing with the development of science, technology and art in this current era. The method of implementing this training is by simulation and practice of photography and graphic design. Stages The activity begins with a classical presentation of the material, explaining photography techniques using simple tools such as smartphones. Furthermore, providing assistance in the practice of photography and graphic design. Graphic design assistance is carried out using a simple application available on a smartphone to produce Lombok food promotion posters. The photographic work produced becomes the task of cultural arts and the poster work made by students becomes the assessment of the tourism work program..
Prediksi Penjualan Produk Unilever Menggunakan Metode Regresi Linear Anthony Anggrawan; Hairani Hairani; Nurul Azmi
Jurnal Bumigora Information Technology (BITe) Vol 4 No 2 (2022)
Publisher : Prodi Ilmu Komputer Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v4i2.2416

Abstract

Barokah Shop is a retail store that sells various basic necessities for daily needs. Too much inventory will result in losses such as storage costs and the possibility of a decrease in the quality of goods. On the other hand, a small amount of inventory will reduce a larger profit. This study aims to build a web-based Unilever sales prediction system using a simple linear regression method. Testing the accuracy of the prediction results of sales of Unilever products using MEA and MAPE to see the level of error in the prediction results. The dataset uses Unilever product sales data for 15 months, from January 2021 to March 2022. The dataset is divided into 12 months as training data and 3 months as testing data. Prediction results in the next 3 periods of each type of product produce the same value between the system results and the results of manual linear regression calculations. Testing the error rate on the prediction results for 3 periods, namely January to March 2022, each Ax Deodorant, Bango Kecap, Buavita, Citra Lotion, Citra Soap, Clear Shampoo, Sariwangi, Sunsilk Conditioner, Vixal and Wall's Ice Cream products belong to the category of very accurate forecasting results. With the smallest MAPE value in Sunsilk Conditioner products of 1%. Thus, the linear regression method is very accurate for predicting sales of Unilever types goods.
Prediction of Electricity Usage with Back-propagation Neural Network Anthony Anggrawan; Hairani Hairani; M. Ade Candra
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 1 No 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (785.334 KB) | DOI: 10.30812/ijecsa.v1i1.1722

Abstract

The use of electricity has become a need that is increasing day by day. So it is not surprising that the problem of using electricity has attracted the attention of many researchers to research it. Electricity users make various efforts and ways to save on the use of electrical energy. One of them is saving electricity usage by electricity users using electrical energy-efficient equipment. That is why the previous research confirms the need for interventions to reduce the use of electrical energy. Therefore, this study aims to predict electricity use and measure the performance of the anticipated results of electricity use. This study uses the back-propagation method in predicting the use of electricity. This study concluded that the backpropagation architectural model with better performance is the six hidden layer architecture, 0.4 learning rate, and the Root Means Square Error (RMSE) value of 0.203424. Meanwhile, the training data test results get the best architectural model on hidden layer 8 with a learning rate of 0.3 with an RMSE performance value of 0.035811. The prediction results show that the prediction of electricity consumption is close to the actual data of actual electricity consumption.
Improvement Performance of the Random Forest Method on Unbalanced Diabetes Data Classification Using Smote-Tomek Link Hairani Hairani; Anthony Anggrawan; Dadang Priyanto
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1069

Abstract

Most of the health data contained unbalanced data that affected the performance of the classification method. Unbalanced data causes the classification method to classify the majority data more and ignore the minority class. One of the health data that has unbalanced data is Pima Indian Diabetes. Diabetes is a deadly disease caused by the body's inability to produce enough insulin. Complications of diabetes can cause heart attacks and strokes. Early diagnosis of diabetes is needed to minimize the occurrence of more severe complications. In the diabetes dataset used, there is an imbalanced data between positive and negative diabetes classes. Diabetes negative class data (500 data) is more than diabetes positive class (268), so it can affect the performance of the classification method. Therefore, this study aims to apply the Smote-Tomeklink and Random Forest methods in the classification of diabetes. The research methodology used is the collection of diabetes data obtained from Kaggle, as many as 768 data with eight input attributes and 1 output attribute as a class, pre-processing data is used to balance the dataset with Smote-Tomeklink, classification using the random forest method, and performance evaluation based on accuracy, sensitivity, precision, and F1-score. Based on the tests conducted by dividing data using 10-fold cross-validation, the Random Forest algorithm with Smote-TomekLink gets the highest accuracy, sensitivity, precision, and F1-score compared to Random Forest with Smote. The Random Forest algorithm with Smote-Tomeklink has 86.4% accuracy, 88.2% sensitivity, 82.3% precision, and 85.1% F1-score. Thus, using Smote-Tomeklink can improve the performance of the random forest method based on accuracy, sensitivity, precision, and F1-score.
Application of KNN Machine Learning and Fuzzy C-Means to Diagnose Diabetes Anthony Anggrawan; Mayadi Mayadi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 2 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2777

Abstract

The disease is a common thing in humans. Diseases that attack humans do not know anyone and do not know age. The disease experienced by a person starts from an ordinary level until it can be declared severe to the point of being at risk of death. In this study, the early diagnosis was carried out related to diabetes, where diabetes is a condition in which the sufferer’s body has low sugar levels above normal. Symptoms experienced by sufferers include frequent thirst, frequent urination, frequent hunger, and weight loss. Based on these problems, a system is needed that can quickly find out the diagnosis experienced by a patient. This research aimed to diagnose diabetes early on based on early symptoms. The methods used are KNN and web-based fuzzy C-means. Creating a web-based system can represent medical personnel experts in a fast-diagnosing approach to diabetes. This system was a computer program embedded with the knowledge of the characteristics of diabetes. The results of testing the KNN and Fuzzy C-means applications and methods get an accuracy of 96% for the KNearest Neighbor method, while for the Fuzzy C-Means method with Confusion Matrix calculations, an accuracy of 96% is obtained, so it can be concluded that the Fuzzy C-means method Means better than the K-Nearest Neighbor method.
Design of Field Rental System on Web-Based Garuda Mataram Badminton Club Khairan Marzuki; Anthony Anggrawan; Helna Wardhana; Lalau Ganda Rady Putra; Canggih Wahyu Rinaldi
JURNAL TEKNIK INFORMATIKA Vol 16, No 1 (2023): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v16i1.27927

Abstract

Residents of West Nusa Tenggara, Mataram City, mostly like badminton. GOR Badminton Garuda is a sports building that provides sports field rental services specifically for badminton (badminton) in the city of Mataram. Rental management at the Garuda Badminton Building is still carried out conventionally, namely by manually recording the list of field tenants in the rental book, resulting in difficult data searches and frequent schedule recording errors. In addition, making reports takes a long time because they are still made from rental books. In terms of field reservation, it is also considered less effective because tenants have to come to the field. The solution provided is to create a web-based online field rental information system using the Waterfall method. The results of creating this information system can make it easier for admins to manage rentals and make it easier for the public to get information and field booking services online. Based on Usability testing using the ISO 9126 model, the Field Rental Information System at the Garuda Badminton Sports Hall is in good criteria with a percentage of 83%.
Comparison of Machine Learning Methods for Classifying User Satisfaction Opinions of the PeduliLindungi Application Putu Tisna Putra; Anthony Anggrawan; Hairani Hairani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 3 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.2860

Abstract

Since the emergence of the Covid-19 virus, the Indonesian government urged people to study, work, and worship or work from home. The social restriction policy has changed people's behavior which requires physical distance in social interaction. The government developed an application to minimize the spread of Covid-19, namely the PeduliLindungi application. The PeduliLindungi application is a tracking application to prevent the spread of Covid-19. The government's policy of implementing the PeduliLindungi application during Covid-19 aroused pros and cons from the public. The volume of PeduliLindungi application review data on Google Play was increasing, so manual analysis could not be done. New analytical approaches needed to be carried out, such as sentiment analysis. This research aimed to analyze user reviews of the PeduilLindungi application using classification methods, namely Support Vector Machine (SVM), Random Forest, and Naïve Bayes. The methods used were Synthetic Minority Oversampling Technique (SMOTE), Random Forest, SVM, and Naïve Bayes. SMOTE was used to balance user review data on the PeduliLindungi application. After the data had been balanced, classification was carried out. The results of this study showed that the Random Forest method with SMOTE got better accuracy than the SVM and Naive Bayes methods, which was 96.3% based on the division of training and testing data using 10-fold cross-validation. Thus, using the SMOTE method could improve the accuracy of classification methods in classifying opinions of user satisfaction with the PeduliLindungi application.
Co-Authors Abdul Rahim Ahmat Adil Alfilail, Nur Anggriani, Rini Aprilia Dwi Dayani Ariq, Tomy Ayu Dasriani, Ni Gusti Azhar, Raisul Azhari Azhari Bidari Andaru Widhi Cahyadi, Irwan Canggih Wahyu Rinaldi Cecep Kusmana christofer satria Christofer Satria Dadang Priyanto Dadang Pyanto Dafa Awanta Dayani, Aprilia Dwi Dedi Aprianto Dewa Ayu Oki Astarini Diah Supatmiwati Dian Syafitri Chani Saputri Dias Nabila Huda Didiharyono, D. Donny Kurniawan Dwi Kurnianingsih Dyah Susilowati Dyah Susilowati Efrizoni, Lusiana Elyakim Nova Supriyedi Patty, Elyakim Nova Supriyedi Erwin Suhendra Fadiel Rahmad Hidayat Hairani Hairani Haryono Haryono Hasbullah Hasbullah Hasbullah Helna Wardhana Hengki Tamando Sihotang Herawati, Baiq Candra Hilda Hastuti Huda, Dias Nabila Husain Husain I Nyoman Subudiartha I Nyoman Yoga Sumadewa I Nyoman Yoga Sumadewa Ikang Murapi Irwan Cahyadi Jean Suciasti Gunawan Junendri Ardian Kamil, Wahyu Katarina Katarina Khairan marzuki Khasnur Hidjah Kurniadin Abd Latif Lalau Ganda Rady Putra Lalu Ganda Rady Putra Lanang Sakti Lutfie, Muhammad Hilal Mumtaz M Najmul Fadli M. Ade Candra M. Thontowi Jauhari Mardedi, Lalu Zazuli Azhar Mayadi Mayadi Mayadi Mayadi Miswaty, Titik Ceriyani Mokhammad Nurkholis Abdillah Muhammad Innuddin Muhammad Ridho Akbar Muhammad Rosikhu MUHAMMAD TAJUDDIN Muhammad Zaki Pahrul Hadi Muhammad Zulfikri Muhsin, Lalu Busyairi Nurhidayati, Maulida Nurul Azmi Nurul Hidayah Peter Wijaya Sugijanto Primajati, Gilang Purnama, Baiq Kartika Putu Tisna Putra R. Ayu Ida Aryani Raden Bagus Faizal Irani Sidharta Rahmat Maulana Rahmawati, Lela Rahmiati, Baiq Fitria Rini Anggriani Rini Anggriani Riosatria Riosatria Riosatria, Riosatria Santoso, Heroe Sarjon Defit Satuang Satuang Sirojul Hadi Siti Soraya Sri Astuti Iriyani Sugijanto, Peter Wijaya Sunardy Kasim Supriantono, Herman Sutarman Syahrir, Moch. Syamsurrijal Syamsurrijal Tomi Tri Sujaka Triwijoyo, Bambang Krismono v, Sovian Veithzal Rivai Zainal Wayan Canny Naktiany Wenny Wijaya Wiya Suktiningsih Zulkipli Zulkipli