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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL PENGABDIAN KEPADA MASYARAKAT Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JMM (Jurnal Masyarakat Mandiri) ILKOM Jurnal Ilmiah JETL (Journal Of Education, Teaching and Learning) JTIM : Jurnal Teknologi Informasi dan Multimedia Jurnal Publikasi Ilmu Komputer dan Multimedia
Nurhopipah, Ade
Department Of Informatics, Universitas Amikom Purwokerto
Aerospace Engineering Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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STIMULASI POTENSI ANAK MELALUI KEGIATAN TOUR THE ANIMATOR TALENT Nurhopipah, Ade; Hasanah, Uswatun; Arifudin, Dani; Krisno, Krisno; Ferdiyansyah, Achmad; Mutiara, Dwi Ayu
JMM (Jurnal Masyarakat Mandiri) Vol 4, No 2 (2020): JUNI
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (517.351 KB) | DOI: 10.31764/jmm.v4i2.1822

Abstract

Abstrak: Seiring perkembangan informasi dan teknologi yang sangat pesat, animator adalah profesi yang semakin populer di bidang multimedia. Adanya program pengenalan tentang profesi animator diharapkan dapat membantu para pendidik menstimulus anak-anak dalam mengidentifikasi minat dan bakatnya. Dengan meninjau pentingnya upaya stimulasi potensi anak-anak di bidang tersebut maka kegiatan “Tour The Animator Talent”  diselenggarakan untuk anak usia Sekolah Dasar di Purwokerto dan sekitarnya. Kegiatan ini berisi pemaparan materi dengan metode student center dan workshop pembuatan animasi sederhana dengan metode project-based learning. Pada evaluasi penguasaan materi diperoleh rata-rata keberhasilan sebesar 74%, sedangkan pada evaluasi workshop diperoleh rata-rata keberhasilan sebesar 82 % dengan menggunakan tolak ukur aspek-aspek yang telah ditentukan penyelenggara.Abstract:  Along with the rapid development of information and technology, animator is an increasingly popular profession in the multimedia field. The introduction of the animator profession program is expected to help educators stimulate children in identifying their interests and talents. By reviewing the importance of stimulating children's potential in this field, the "The Animator Talent" event was held for elementary school-age children in Purwokerto and surrounding areas. This activity contains material exposure by the student center method and a workshop on making simple animations using the project-based learning method. In the evaluation of mastery of the material obtained an average success of 74%, while in the evaluation of the workshop obtained an average success of 82% by using benchmarks aspects that have been determined by the organizer.
CNN Hyperparameter Optimization using Random Grid Coarse-to-fine Search for Face Classification Nurhopipah, Ade; Larasati, Nurriza Amalia
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 1, February 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i1.1185

Abstract

Convolutional Neural Network (CNN) is a recently used popular machine learning technique to classify images. However, choosing an optimum and efficient architecture is an inevitable challenge. The research goal was to implement CNN on face classification from low quality CCTV footage. The best model was gained from the hyperparameter optimization process used on CNN structure. The optimized hyperparameters were those connected to the structure network including activation function, the number of kernel, the size of kernel, and the number of nodes on the fully connected layers. Hyperparameter optimization strategy used was random grid coarse-to-fine search optimization approach. This approach combined random search, grid search, and coarse-to-fine technique that was easily and efficiently applied, yet worked well. Exhaustive-random search process was done by evaluating all selected activation functions and choosing another hyperparameters randomly. This was based on the assumption that activation functions were the most related hyperparameter to the model. The SELU activation function used in the next step was the one with the best average performance. Grid coarse-to-fine was conducted to optimize the number of kernel and the number of node on fully connected layer, while grid search was conducted to optimize the kernel size. This process aimed to locate optimal value gradually in hyperparameter which had high-dimensional space. Evaluation of the model resulted from the optimum hyperparameter was 97,56%.
Motion Detection and Face Recognition for CCTV Surveillance System Ade Nurhopipah; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 2 (2018): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.18198

Abstract

Closed Circuit Television (CCTV) is currently used in daily life for a variety purpose. Development of the use of CCTV has transformed from a simple passive surveillance into an integrated intelligent control system. In this research, motion detection and facial recognation in CCTV video is done to be a base for decision making to produce automated, effective and efficient integrated system. This CCTV video processing provides three outputs, a motion detection information, a face detection information and a face identification information. Accumulative Differences Images (ADI) used  for motion detection, and Haar Classifiers Cascade used  for facial segmentation. Feature extraction is done with Speeded-Up Robust Features (SURF) and Principal Component Analysis (PCA). The features was trained by Counter-Propagation Network (CPN). Offline tests performed on 45 CCTV video. The test results obtained a motion detection success rate of 92,655%, a face detection success rate of 76%, and a face detection success rate of 60%. The results concluded that the process of faces identification through CCTV video with natural background have not been able to obtain optimal results. The motion detection process is ideal to be applied to real-time conditions. But in combination with face recognition process, there is a significant delay time.
Dataset Splitting Techniques Comparison For Face Classification on CCTV Images Ade Nurhopipah; Uswatun Hasanah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 4 (2020): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.58092

Abstract

The performance of classification models in machine learning algorithms is influenced by many factors, one of which is dataset splitting method. To avoid overfitting, it is important to apply a suitable dataset splitting strategy. This study presents comparison of four dataset splitting techniques, namely Random Sub-sampling Validation (RSV), k-Fold Cross Validation (k-FCV), Bootstrap Validation (BV) and Moralis Lima Martin Validation (MLMV). This comparison is done in face classification on CCTV images using Convolutional Neural Network (CNN) algorithm and Support Vector Machine (SVM) algorithm. This study is also applied in two image datasets. The results of the comparison are reviewed by using model accuracy in training set, validation set and test set, also bias and variance of the model. The experiment shows that k-FCV technique has more stable performance and provide high accuracy on training set as well as good generalizations on validation set and test set. Meanwhile, data splitting using MLMV technique has lower performance than the other three techniques since it yields lower accuracy. This technique also shows higher bias and variance values and it builds overfitting models, especially when it is applied on validation set.
Behind the Mask: Detection and Recognition Based-on Deep Learning Ade Nurhopipah; Irfan Rifai Azziz; Jali Suhaman
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 1 (2022): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.72075

Abstract

COVID-19 prevention procedures are executed to support public services and business continuity in a pandemic situation. Manual mask use monitoring is not efficient as it requires resources to monitor people at all times. Therefore, this task can be supported by automated surveillance systems based on Deep Learning. We performed mask detection and face recognition for a real-environment dataset. YOLOV3 as a one-stage detector was implemented to simultaneously generate a bounding box of the face area and class prediction. In face recognition, we compared the performance of three pre-trained models, namely ResNet152V2, InceptionV3, and Xception. The mask detection showed promising results with MAP=0.8960 on training and MAP=0.8957 on validation. We chose the Xception model for face recognition because it has equal quality as ResNet152V2 but has fewer parameters. Xception achieved a minimal loss value in the validation of 0.09157 with perfect accuracy on facial images larger than 100 pixels. Overall the system delivers promising results and can identify faces, even those behind the mask.
PEMBELAJARAN PEMROGRAMAN BERBASIS PROYEK UNTUK MENGEMBANGKAN KEMAMPUAN COMPUTATIONAL THINKING ANAK Ade Nurhopipah; Indra Alan Nugroho; Jali Suhaman
JURNAL PENGABDIAN KEPADA MASYARAKAT Vol 27, No 1 (2021): JANUARI-MARET
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jpkm.v27i1.21291

Abstract

Orientasi pembelajaran pemrograman pada anak tidak hanya bertujuan untuk menghasilkan produk teknologi, namun juga untuk membentuk computational thinking sejak dini. Kegiatan Pembelajaran Pemrograman Berbasis Proyek adalah metode pembelajaran melalui proyek untuk mengembangkan code literacy anak dengan memperkenalkan mereka pada dunia pemrograman dan aktivitasnya. Peserta kegiatan ini adalah siswa Sekolah Dasar di Purwokerto. Kegiatan dirancang sedemikian rupa agar menarik dan menyenangkan, sehingga siswa menganggap proses pembelajaran ini sebagai arena bermain. Bahasa pemrograman yang digunakan adalah Scratch dari MIT Media Lab. Setelah kegiatan ini, terdapat kemajuan peserta dalam aspek pengetahuan dan minat mereka dalam pemrograman. Peningkatan aspek pengetahuan peserta mencapai 22,83% dan peningkatan aspek minat peserta sebesar 8%. Setelah mengikuti kegiatan ini, penguasaan Scratch peserta adalah 71%. Dengan pencapaian tersebut, fasilitator menilai kegiatan ini cukup dapat mengenalkan penggunaan Scratch kepada peserta. Penilaian terhadap antusiasme peserta sebesar 88% menandakan bahwa peserta menyukai dan menikmati proses pembelajaran serta menunjukkan bahwa kegiatan telah berjalan dengan baik dan memuaskan.Kata Kunci: Anak; Computational Thinking;Belajar Pemrograman Berbasis Proyek; Scratch.
PEMBELAJARAN ILMU KOMPUTER TANPA KOMPUTER (UNPLUGGED ACTIVITIES) UNTUK MELATIH KETERAMPILAN LOGIKA ANAK Ade Nurhopipah; Jali Suhaman; Moza Tri Humanita
JMM (Jurnal Masyarakat Mandiri) Vol 5, No 5 (2021): Oktober
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (565.611 KB) | DOI: 10.31764/jmm.v5i5.5295

Abstract

Abstrak: Pembelajaran ilmu komputer untuk anak-anak telah menjadi agenda pendidikan di banyak negara karena dapat melatih berpikir logis dan kreatif. Namun upaya ini seringkali tidak dapat diwujudkan karena keterbatasan sumber daya serta akses terhadap perangkat komputer. Unplugged Activities merupakan pendekatan pembelajaran konsep dasar ilmu komputer tanpa menggunakan komputer melalui permainan menarik. Kegiatan ini berbasis aktivitas fisik, dapat dilakukan dalam berbagai format dengan menggunakan instrumen sederhana, murah dan mudah ditemukan. Meninjau manfaat tersebut rangkaian kegiatan “Pengenalan Permainan Edukatif Berdasarkan Ilmu Komputer” dilakukan untuk memperkenalkan prinsip, metode, instrumen, dan referensi Unplugged Activities. Peserta kegiatan adalah 63 orang guru, pegiat pendidikan, dan orang tua dari dari berbagai kota. Para peserta selanjutnya menerapkan kegiatan ini kepada anak-anak dan siswanya. Evaluasi dilakukan dengan membandingkan penilaian mandiri peserta sebelum dan sesudah acara, serta penilaian terhadap implementasi Unplugged Activities terhadap anak. Hasil evaluasi menunjukan terdapatnya peningkatan pemahaman peserta dalam pengetahuan dasar Unplugged Activities sebesar 35,7%. Dalam implementasinya, kegiatan ini dapat dinikmati, difahami dan membuat anak-anak termotivasi mempraktekan permainan.Abstract: Learning computer science for children has become an educational agenda in many countries because it can train logical and creative thinking. However, this effort often cannot be realized due to limited resources and access to computer devices. Unplugged Activities is an approach to learning the basic concepts of computer science without using a computer through interesting games. This activity is based on physical activity, can be done in various formats using simple, inexpensive, and easy-to-find instruments. Because of these benefits, the series of activities "Introduction to Educational Games Based on Computer Science" was conducted to introduce the principles, methods, instruments, and references to Unplugged Activities. The participants of the activity were 63 teachers, education activists, and parents from various cities. The participants then applied this activity to their children and students. Evaluation is done by comparing the participants' self-assessment before and after the event and assessing the implementation of Unplugged Activities for children. The evaluation results showed an increase in participants' understanding of the basic knowledge of Unplugged Activities by 35.7%. In its implementation, this activity can be enjoyed, understood, and motivated children to practice the game.
Exploring Indirect Aspects in Motivation and Academic Achievement During The Pandemic Ade Nurhopipah; Ida Nuraida; Jali Suhaman
JETL (Journal of Education, Teaching and Learning) Vol 6, No 2 (2021): Volume 6 Number 2 September 2021
Publisher : STKIP Singkawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (285.94 KB) | DOI: 10.26737/jetl.v6i2.2590

Abstract

Online learning methodologies are the most influential factor in educational success. However, it cannot be denied that there are non-technical aspects that indirectly affect students' motivation and academic achievement after the Covid-19 pandemic occurred. This study involves paired t-tests, regression tests, and partial t-tests to analyze the factors that can indirectly shape student motivation and academic achievement in Indonesia's higher schools. The factors studied were related to economic condition, health, habits, and social interaction. The result shows significant changes in learning motivation, economic and health conditions, student interactions with friends and lecturers, student involvement in student activity units and religious activities, use of social media, and time spent reading. The Grade Point Accumulative (GPA) before the pandemic was influenced by learning motivation. However, during the pandemic, the GPA was not significantly affected by learning motivation. Before the pandemic, family engagement and student involvement in religious activities significantly influence the GPA. Meanwhile, the factors that influence learning motivation during the pandemic are student involvement in social activities, interaction with lecturers, health conditions, and time spent reading.
PERBANDINGAN METODE RESAMPLING PADA IMBALANCED DATASET UNTUK KLASIFIKASI KOMENTAR PROGRAM MBKM Ade Nurhopipah; Cindy Magnolia
Jurnal Publikasi Ilmu Komputer dan Multimedia Vol 2 No 1 (2023): Januari : Jurnal Publikasi Ilmu Komputer dan Multimedia
Publisher : Sekolah Tinggi Ilmu Ekonomi Trianandra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupikom.v2i1.862

Abstract

Imbalanced dataset yaitu kondisi di mana dataset didominasi oleh salah satu kelas adalah permasalahan yang umum ditemukan dalam aplikasi di dunia nyata. Pada penelitian ini, permasalahan tersebut terjadi pada dataset yang dikumpulkan untuk klasifikasi empat jenis komentar publik terhadap program Merdeka Belajar Kampus Merdeka (MBKM). Dataset tersebut memiliki Imbalanced Rasio yang tinggi sebesar 5:1 dan kinerja klasifikasi yang rendah dengan F-Measure di antara 0,6209 sampai 0,6672. Masalah ini mendasari tujuan penelitian, yaitu mencoba mengeksplorasi beberapa teknik resampling untuk melihat pengaruhnya terhadap kinerja model klasifikasi. Metode resampling yang diteliti adalah undersampling dengan Near Miss dan Tomek Links, oversampling dengan SMOTE dan ADASYN, dan kombinasi undersampling dan oversampling dengan Random Combination Sampling (RCS). Penelitian ini menggunakan empat classifier yaitu Random Forest, Logistic Regression, SVM dan MLP untuk melihat stabilitas efek metode resampling. Berdasarkan analisis yang dilakukan, dapat disimpulkan bahwa metode Near Miss pada penelitian ini tidak memberikan efek positif dalam peningkatan kinerja model. Sebaliknya, metode lainnya dapat memperbaiki kinerja model classiifier dengan meningkatkan nilai F-Measure. Kinerja terbaik diperoleh pada model klasifikasi SVM dengan dataset hasil resampling metode SMOTE. Setelah melalui analisis optimasi model dan metode resampling diperoleh nilai F-Measure maksimal sebesar 0.9524.
Prediksi Persentase Body Fat Menggunakan Algoritma CART dan M5’ Uswatun Hasanah; Ade Nurhopipah
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 4 No 4 (2023): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v4i4.316

Abstract

Body Fat Percentage (BFP) is a measurement of total body fat that is used as an accurate measurement for the diagnosis of obesity. BFP measurement is sometimes difficult and inconvenient to perform, even though the picture of BFP’s value is very important for someone to find out the chances of being obese. To overcome this, data mining techniques can be used to measure the predictions of BFP values in a more practical way. This study implements data mining techniques, namely the CART and M5’ algorithm to predict a person's BFP value based on his/her body measurement. The CART algorithm uses the sample average values at leaf nodes to make numerical predictions, while the M5' algorithm builds a regression model for each leaf node with a hybrid approach. Regression trees provide a simple way of explaining the relationship between features and numerical results, but more complex model trees also provide more accurate results. In this study, the results show that the M5' algorithm is superior to the BFP dataset with a correlation value of 0.86 and an MAE value of 3.86.
Co-Authors Agus Harjoko Anan Widianto Arif Munandar Arifudin, Dani Cindy Magnolia Ferdiyansyah, Achmad Ida Nuraida Indra Alan Nugroho Irfan Rifai Azziz Jali Suhaman Jali Suhaman Jali Suhaman Jali Suhaman Jali Suhaman Krisno, Krisno Larasati, Nurriza Amalia Moza Tri Humanita Mutiara, Dwi Ayu Uswatun Hasanah Uswatun Hasanah Uswatun Hasanah Wiga Maulana Baihaqi