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All Journal Jurnal Ilmiah Informatika Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Jurnal Transformatika Jurnas Nasional Teknologi dan Sistem Informasi E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JPM (Jurnal Pemberdayaan Masyarakat) Jurnal BALIRESO ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer KOMPUTIKA - Jurnal Sistem Komputer Celebes Abdimas: Jurnal Pengabdian Kepada Masyarakat Buletin Ilmiah Sarjana Teknik Elektro G-Tech : Jurnal Teknologi Terapan Journal of Information Technology and Its Utilization Indian Journal of Forensic Medicine & Toxicology International Journal of Engineering, Science and Information Technology Jurnal PkM (Pengabdian kepada Masyarakat) Caraka : Indonesia Journal of Communication Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Celebes Journal of Community Services KOPEMAS
Dolly Indra
Universitas Muslim Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Pengenalan Angka Tulisan Tangan Menggunakan Jaringan Syaraf Tiruan Herman Herman; Lukman Syafie; Dolly Indra
ILKOM Jurnal Ilmiah Vol 10, No 2 (2018)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v10i2.317.201-206

Abstract

Current technological developments spur the application of pattern recognition in various fields, such as the introduction of signature patterns, fingerprints, faces, and handwriting. Human handwriting has differences between one another and often handwriting is difficult to read or difficult to recognize and this can hamper daily activities, such as transaction activities that require handwriting. Even one of the biometric features of everyone is handwriting. One method that can be used to recognize handwriting patterns in the field of computer science is artificial neural networks (ANN) with the learning algorithm is backpropagation. Artificial neural networks are able to recognize something based on the past. This means that past data will be studied so as to be able to make decisions on new data. To recognize handwriting patterns using artificial neural networks, the characteristics of handwritten objects are extracted using an invariant moment. The results of training using artificial neural networks indicate that the correlation coefficient value is obtained on the number of hidden layer neurons by 30. The highest correlation coefficient value is 0.61382. The test results on the test data obtained an accuracy rate of 11.67% of the total test data.
Detection System of Strawberry Ripeness Using K-Means Dolly Indra; Ramdan Satra; Huzain Azis; Abdul Rachman Manga; Harlinda L
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1054.33-39

Abstract

Strawberry is one type of fruit that is favored by the people of Indonesia. The detection process to identify strawberries can be done by utilizing advances in computer technology, One of them is in the field of digital image processing. In this study, we made a strawberry ripeness detection system using the values of Red, Green and Blue as the reference values, while for identification in determining the type of classification using the K-Means algorithm that uses the Euclidean distance difference as the reference. Based on the results of testing using the K-Means algorithm on 51 strawberry images consisting of ripe, semi ripe and raw fruit yielding an accuracy rate of 82.14%, we also conducted tests other than strawberry images as many as 8 images yielded an accuracy rate of 100%.
DESIGN WEB-BASED ELECTRICAL CONTROL SYSTEM USING RASPBERRY PI Dolly Indra; Tasmil Tasmil; Herman Herman; St. Hajrah Mansyur; Erick Irawadi Alwi
Journal of Information Technology and Its Utilization Vol 2, No 1 (2019)
Publisher : Sekolah Tinggi Multi Media (STMM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30818/jitu.2.1.2275

Abstract

The use of current website technology can be applied as a control and monitoring system, which is used to control electrical devices, so that the user can only control the PC or smartphone that has been connected to Wi-Fi or the Internet. In this case the control uses the Raspberry Pi Mini PC which has several advantages such as low power and is relatively easy when connected with a web server compared to a microcontroller. By utilizing the Raspberry Pi Mini PC as a web server, it can replace PC functions in general. The results in this study are the Electric Control System that has been made capable of controlling 4 AC voltage electronics as well as 4 relays with each relay capable of bearing a maximum load of 2200 watts using a power supply on the Raspberry Pi which has a minimum of 0.7 amperes and Control of electrical load can be done within a distance of 0 meters - 15 meters from the wireless router
Pengembangan Peningkatan Produktivitas dan Pemasaran UKM Abon Telur sebagai Oleh-Oleh Khas Malino di Desa Lonjoboko Kecamatan Parangloe Kabupaten Gowa Purnawansyah Purnawansyah; Dolly Indra; Lilis Nur Hayati; Fery Setyo Aji; Rezky Anugrah
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 14, No 1 (2023): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v14i1.5973

Abstract

Tujuan program pengabdian yang kami lakukan yaitu memberikan penyuluhan, dan simulasi tentang pemasaran produk, memberikan pelatihan bagi mitra desa Lonjoboko tentang kewirausahaan dalam memasarkan produk berbasis online dan mendesain kemasan yang menarik dan praktis. Metode dalam pelaksanaaan kegiatan ini adalah memfasilitasi dengan penyuluhan, simulasi dan pelatihan bagi para UKM dengan mewujudkan masyarakat sejahtera dan pandai dalam memasarkan produk dengan layanan sistem informasi berbasis online di desa Lonjoboko Kabupaten Gowa dalam bentuk pelatihan. Luarannya Mitra mendapatkan modul pelatihan manajemen kewirausahaan berbasis online untuk memasarkan produknya, mitra mampu mandiri dalam mengimplementasikan dan terampil dalam pemasaran produk, Software Aplikasi Web Sistem Informasi pemasaran produk.
Pemanfaatan Informasi dan Teknologi Komunikasi di Pesantren Mizanul Ulum Dolly Indra; Erick Irawadi Alwi
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 15, No 1 (2024): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v15i1.15037

Abstract

Pesantren Mizanul Ulum mempunyai permasalahan dalam kegiatan pendidikan yaitu model pembelajaran  yang diberikan guru kepada para santri bersifat konvensional dan belum memanfaatkan teknologi komputer. Pengabdian yang kami lakukan bertujuan agar mitra mendapat pengetahuan tentang manfaat informasi dan teknologi komunikasi dengan cara melakukan  sosialisasi  dan pelatihan kepada para guru.  Sosialisasi dan pelatihan  modul pembelajaran terdiri dari pembelajaran Word, Excel, Power Point dan penggunaan Google Forms. Untuk mengetahui hasil dari sosialisasi ini, kami melakukan evaluasi terhadap guru melalui pretest dan posttest dengan jumlah soal sebanyak 30 soal dengan skor maksimal 30. Peserta yang terlibat dalam sosialisasi sebanyak 16 orang guru. Berdasarkan hasil evaluasi diketahui bahwa skor pretest adalah 334 dan skor posttest adalah 414. Rata-rata skor pretest adalah 20,875 dan posttest adalah 25,875 sedangkan skor pretest maksimum adalah 26 dan posttest adalah 28, skor minimum untuk pretest adalah 15 dan posttest adalah 23. Kegiatan sosialisasi yang kami lakukan ini pada Pesantren Mizanul Ulum Sanrobone Kabupaten Takalar mampu meningkatkan pengetahuan para guru.
Literasi dan Pendampingan Pengelolaan Website Fakultas Ekonomi dan Bisnis Untuk Peningkatan Peringkat UMI di Webometrics Berdasarkan Aspek Penilaian Visibility Irawati Irawati; Purnawansyah Purnawansyah; Dolly Indra
Ilmu Komputer untuk Masyarakat Vol 5, No 1 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkomas.v5i1.2292

Abstract

Webometrics adalah metodologi yang digunakan untuk memberikan pemeringkatan pada perguruan tinggi di seluruh dunia. Sebagai salah satu perguruan tinggi swasta terbesar dan terbaik di Indonesia Timur, UMI berupaya untuk meningkatkan mutu pendidikannya setiap tahun. Setiap kegiatan yang dilakukan oleh UMI akan tercatat website pemeringkatan universitas dunia yang disebut Webometrics. Agar dapat menunjang indikator-indikator pencapaian webometrics UMI, maka civitas akademika UMI perlu memahami tentang pentingnya pengetahuan tentang indikator yang dinilai oleh Cybermetrics Lab sebagai metode pemeringkatan perguruan tinggi. Kegiatan yang dilakukan berupa Sosialisasi dan workshop  Webometrix tentang Visibility Impact (50%), jumlah eksternal link unik yang terhubung dengan domain web milik perguruan tinggi. 
Implementasi Sistem Penghitung Kendaraan Otomatis Berbasis Computer Vision Indra, Dolly; Herman, Herman; Budi, Firman Shantya
Komputika : Jurnal Sistem Komputer Vol 12 No 1 (2023): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v12i1.9082

Abstract

The development of computer technology today is very helpful for humans in completing their work in various fields. One application of computer technology i.e., in the field of computer vision which has a very important role for object recognition. In this study, we designed a computer vision-based automatic vehicle counting system. The system that we created uses the MobileNetV2 Single Shot Multibox Detector (SSD) which is placed on the Raspberry Pi 4 to carry out the process of classifying cars and motorcycles and the raspberry pi 4 also functions as a system controller. This automatic vehicle counter system has been integrated between Raspberry Pi 4 and a mobile application on a smartphone where the smartphone functions to display information such as day, date, month, year and together with the number of cars and motorcycles. We tested this automatic vehicle counting system on steam services (car and motorcycle washing) for 3 days where 10 vehicles were collected every day. The test results show that the system is capable of detecting cars and motorcyles with an average accuracy rate of 46.6%. Keywords – Vehicle Detection, SSD-MobileNet V2, Computer Vision, Raspberry Pi, Smartphone
Pengenalan Huruf BISINDO Menggunakan Chain Code Contour dan Naive Bayes Indra, Dolly; Hayati, Lilis Nur; Irja, Mulianty Cipta
Komputika : Jurnal Sistem Komputer Vol 13 No 1 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i1.10360

Abstract

Digital image processing juga dikenal sebagai pengolahan citra digital merupakan suatu metode yang digunakan untuk memproses atau manipulasi citra digital. Pengolahan citra digital dapat menyelesaikan berbagai bidang permasalahan, salah satunya adalah pengenalan huruf Bahasa Isyarat Indonesia (BISINDO) yang digunakan penyandang tunarungu dan tunawicara dalam berkomunikasi. Adapaun tujuan kami melakukan penelitian ini adalah membangun aplikasi berbasis citra digital yang dapat mengenali huruf BISINDO dari huruf A sampai Z dengan tingkat akurasi kemiripan huruf yang baik. Data huruf BISINDO yang digunakan sebanyak 260 citra dengan pembagian dataset 80% atau 208 citra untuk data latih dan 20% atau 52 citra untuk data uji. Tahapan dalam pengenalan huruf ini diawali dengan melakukan pre-processing dengan menkonversi citra RGB ke grayscale, segmentasi menggunakan thresholding, morfologi opening dan deteksi tepi sobel, peroleh nilai ekstraksi fitur bentuk menggunakan Chain Code Contour. Nilai yang didapatkan dari ekstraksi fitur tersebut akan digunakan pada tahap akhir, yaitu tahap pengenalan citra huruf BISINDO menggunakan metode klasifikasi Naive Bayes. Penelitian ini menggunakan 2 skenario pengujian yaitu skenario database dan skenario diluar database, dimana setiap skenario menggunakan 3 pembagian dataset yaitu 80: 20, 70:30, dan 60:40. Hasil pengujian pada skenario database dengan pembagian dataset 80:20 memperoleh akurasi mencapai 100%, pada pembagian dataset 70:30 akurasi mencapai 92.3%, dan pada pembagian dataset 60:40 akurasi mencapai 88.4%. Untuk skenario diluar database pada pembagian dataset 80:20 memperoleh akurasi mencapai 80.7%, pada pembagian dataset 70:30 akurasi mencapai 73.07%, dan pada pembagian dataset 60:40 akurasi mencapai 75.9%. Berdasarkan hasil pengujian yang telah dilakukan diperoleh akurasi terbaik pada pembagian dataset 80:20, dengan tingkat akurasi pengujian pada skenario database mencapai 100% dan pada skenario diluar database akurasi mencapai 80.7%. Hal ini menunjukkan bahwa metode ekstraksi fitur bentuk Chain Code Contour dan klasifikasi Naive Bayes mampu mengenali huruf BISINDO dengan baik.
Penerapan Metode Random Forest dalam Klasifikasi Huruf BISINDO dengan Menggunakan Ekstraksi Fitur Warna dan Bentuk Indra, Dolly; Hayati, Lilis Nur; Daris, Mega Asfirawati; As'ad, Ihwana; Mansyur, Umar
Komputika : Jurnal Sistem Komputer Vol 13 No 1 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i1.10363

Abstract

Digital image processing is a field of study that focuses on how an image can be formed, processed, and analyzed to generate useful information for humans. In this research, the utilization of digital images is implemented to classify BISINDO (Indonesian Sign Language) letters from A to Z using the Random Forest classification method. The initial stage in the classification of BISINDO letter images involves pre-processing, which includes converting RGB images to grayscale and performing segmentation through three stages: thresholding, morphology, and edge detection using the Prewitt operator. Subsequently, features such as HSV color extraction and metric shape features, as well as eccentricity, are extracted. These extracted feature values are then utilized in the classification stage of BISINDO letter images from A to Z using the Random Forest method. In this study, three data comparison scenarios were employed for testing purposes. The first scenario involved an 80:20 data ratio, which achieved a testing accuracy of 94.2%. The second scenario with a 70:30 data ratio achieved a testing accuracy of 93.6%, while the third scenario with a 60:40 data ratio had a lower accuracy of only 77.9%. Based on the results of our testing, the system developed is capable of effectively classifying BISINDO letters from A to Z using color and shape feature extraction, along with the Random Forest classification method. The best results were obtained in the data comparison scenario of 80:20, achieving an accuracy rate of 94.2%. Keywords – BISINDO, HSV, Metric, Eccentricity, Random Forest.
Classifying BISINDO Alphabet using TensorFlow Object Detection API Hayati, Lilis Nur; Handayani, Anik Nur; Irianto, Wahyu Sakti Gunawan; Asmara, Rosa Andrie; Indra, Dolly; Fahmi, Muhammad
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1692.358-364

Abstract

Indonesian Sign Language (BISINDO) is one of the sign languages used in Indonesia. The process of classifying BISINDO can be done by utilizing advances in computer technology such as deep learning. The use of the BISINDO letter classification system with the application of the MobileNet V2 FPNLite  SSD model using the TensorFlow object detection API. The purpose of this study is to classify BISINDO letters A-Z and measure the accuracy, precision, recall, and cross-validation performance of the model. The dataset used was 4054 images with a size of  consisting of 26 letter classes, which were taken by researchers by applying several research scenarios and limitations. The steps carried out are: dividing the ratio of the simulation dataset 80:20, and applying cross-validation (k-fold = 5). In this study, a real time testing using 2 scenarios was conducted, namely testing with bright light conditions of 500 lux and dim light of 50 lux with an average processing time of 30 frames per second (fps). With a simulation data set ratio of 80:20, 5 iterations were performed, the first iteration yielded a precision result of 0.758 and a recall result of 0.790, and the second iteration yielded a precision result of 0.635 and a recall result of 0.77, then obtained an accuracy score of 0.712, the third iteration provides a recall score of 0.746, the fourth iteration obtains a precision score of 0.713 and a recall score of 0.751, the fifth iteration gives a precision score of 0.742 for a fit score case and the recall score is 0.773. So, the overall average precision score is 0.712 and the overall average recall score is 0.747, indicating that the model built performs very well.
Co-Authors Abdul Rauf Tuasikal Agung, Riski Dewa Ahmad Toni, Ahmad Aldri Frinaldi Amir, Nur Hikmah Anik Nur Handayani Arfan Zainuddin As'ad, Ihwana Astuti, Wistiani Damanhuri, Nor Salwa Daris, Mega Asfirawati Erick Irawadi Alwi Erick Irawadi Alwi Fadly Achmad Farniwati Fattah Fery Setyo Aji Firman Shantya Budi, Firman Shantya Hadyan Mardhi Fadlillah Haerdiansyah Syahnur, Muhammad Harlinda Lahuddin Hayudin Hasnanda Maila Herman Herman Hi. Talib, Juraiz Hidayat, Muh Wahyu Huzain Azis Ihwana As’ad Irawati Irawati Irja, Mulianty Cipta Jafar, Putri Jufriadif Na`am, Jufriadif Julius Santony Kadri Rahmat Suat, Wahyu Kasman Kasman Lilis Nur Hayati lilis nurhayati Lukman Syafie Lutfi Budi Ilmawan Manga, Abdul Rachman Mansyur, St. Hajrah Mardiyyah Hasnawi Muh. Ridwan Rahim Muhammad Al Mubarak Muhammad Arfah Asis Muhammad Farhan Hermansyah Mukarramah, Rifqatul Novanto, Achmad Nur Hayati, Lilis Nurhalima Nurhalima Ode, Nada Kayatri Purnawansyah Purnawansyah Rahmayani, Nurul Ramadan, Syahril Ramdan Satra Ramdaniah Ramdaniah Ramdaniah, Ramdaniah Rezky Anugrah Rifky Saputra Scania, Muhammad Rosa Andrie Asmara Satma, Satma St. Hajrah Mansyur Subhan Ardhiman Sugiarti, Sugiarti Sukur, Widianti Syahnur, Muh. Haerdiansyah Syahrul Mubarak Abdullah Tasmil Tasmil Tasrif Hasanuddin Taufik, Muhammad Asrai Umar Mansyur Umar, Fitriyani Veithzal Rivai Zainal Wahyu Sakti Gunawan Irianto Yuhandri Yuhandri, Yuhandri Yundari, Yundari