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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Building of Informatics, Technology and Science Jurnal AbdiMas Nusa Mandiri Jurnal Pengabdian Masyarakat Indonesia Maju (JPMIM)
Achmad Lukman, Achmad
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Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Environmental Science Library & Information Science Mathematics Nursing Physics Public Health Social Sciences Other

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Clustering Performance Between K-means and Bisecting K-means for Students Interest in Senior High School Seniwati, Erni; Sidauruk, Acihmah; Haryoko, Haryoko; Lukman, Achmad
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3624

Abstract

The interest of high school students is an important thing to do to see the talents of each student based on the academic scores obtained in the first and second semesters. There are two majors of interest in this case study, namely natural and social studies with criteria for natural studies scores including mathematics, chemistry, biology and physics. Meanwhile, the social studies criteria include history, economics, geography and sociology. This research propose comparing of clustering time and accuracy based on manual data from school as a reference of clustering in SMAN 1 Wonosari for 2011/2012 academic year using two clustering methods namely K-means and Bisecting K-Means. The results of this research compare to manual results interest from class teacher, so this work can demonstrate the run time comparison and accuracy of this study. The accuracy result shows 87.5% for both methods but different run times. For bisecting k-means got 0.0229849 seconds to complete the clustering process faster than k-means only got 0.0929448 seconds
Buku Diare Si Nenek Media Literasi Audio Visual Pencegahan Diare Fitrianie, Bella; Lukman, Achmad; Syakur, Abdan Shofu Adlu; Wahyudi, Abdal Rozaq Putra; Ayu, Laras Agesti; Salsabila, Nadia; Faniasih, Rofi
Jurnal Pengabdian Masyarakat Indonesia Maju Vol 3 No 01 (2022): Jurnal Pengabdian Masyarakat Indonesia Maju Volume 03 Nomer 01 Tahun 2022
Publisher : UIMA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33221/jpmim.v3i01.1705

Abstract

Data dari Profil Dinas Kesehatan Jawa Barat bahwa jumlah diare sebanyak 990.832. Dari data tersebut Kabupaten Bogor memiliki jumlah kejadian diare sebanyak 109.820 peringkat tertinggi pertama se-Jawa Barat (1). Alternatif luaran intervensi pada pelaksanaan Praktek Belajar Lapangan (PBL 3) ini adalah Film Dokumenter. Film Dokumenter ini dibuat dengan tujuan untuk memberikan edukasi mengenai penyakit diare pada masyarakat umum sehingga para responden dapat menerapkan perilaku pencegahan penyakit tersebut. Penelitian ini dibuat dengan metode observasi secara langsung dengan mendatangi narasumber yang terpapar diare, kegiatan ini berlangsung dalam jangka waktu April hingga September 2021 di wilayah Kota Bogor, dengan sasaran masyarakat umum kota Bogor. Berdasarkan hasil yang didapat dari pretest terdapat 82,8% orang yang belum mengetahui tentang penyakit diare dan berdasarkan hasil post test terdapat peningkatan pengetahuan masyarakat terhadap penyakit diare dengan melihat nilai akurasi dari pre test dan post test. Kegiatan ini memiliki kekurangan yaitu jaringan internet yang kurang stabil. Materi film documenter yang disampaikan mendapat respon yang cukup baik dari responden. Kesimpulan kegiatan ini adalah untuk memberikan edukasi mengenai penyakit diare pada masyarakat umum sehingga para responden dapat menerapkan perilaku pencegahan penyakit tersebut. Diharapkan kepada masyarakat dapat mempelajari tentang diare dengan menggunakan media literasi audio visual berupa film documenter sehingga dapat meningkatkan pengetahuan masyarakat tentang penyakit diare.
Citra Sitentik Untuk Klasifikasi Buah Menggunakan Algoritma SIFT Descriptor, Bag of Features dan Support Vector Machine Lukman, Achmad; Seniwati, Erni; Riswanto, Eko
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6296

Abstract

Recognizing specific objects assigned to a computer using artificial intelligence of course goes through a training and testing process using machine learning methods, the limited number of datasets makes it difficult for deep learning methods to carry out classification, so to overcome this, other methods are needed, including Scale Invariant Features Transform ( SIFT) which is a method of image processing to extract features from a limited amount of data and combined with a method in machine learning. To overcome the inability of deep learning to use limited datasets, this research uses a combination of SIFT and bag of features to extract features and support vector machine (SVM) to carry out classification. In this study, the aim is to observe the effect of synthetic images on the performance of the combination of SIFT descriptor, Bag of Features and Support Vector Machine algorithms in classifying real fruit images. The dataset involved is a synthetic image in the form of a 3D image that is made into a complete object, then taking random views to make an image that represents the object as training data. Furthermore, for testing data, real images taken from the dataset link in previous research will be used. The number of synthetic datasets that can be collected for each fruit is 150 images, so that the total is 450 images, while the real fruit images consist of 148 apple images, 152 banana images, and 166 orange images, so that the total real images are 466 images. The results of this research show that the highest accuracy was 65.45% with an F1-score reaching 58.45%.
Perbandingan Performa Arsitektur CNN Terhadap Klasifikasi Tumor Otak Menggunakan Data MRI Saputri, Sekar Dewi Harnum; Lukman, Achmad; Irsan, Muhamad
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6710

Abstract

This study discusses the performance comparison of four Convolutional Neural Network (CNN) architectures in brain tumor classification using histopathology images. CNN has proven its effectiveness in improving the accuracy and efficiency of image-based medical diagnosis. This study compares four popular architectures, namely ResNet, AlexNet, InceptionNet, and VGG12, using a histopathology image dataset with a total of 2,145 images divided into training (70%), validation (15%), and testing (15%) subsets. The results show that the VGG12 model achieves the best accuracy of 98.0%, followed by InceptionNet with an accuracy of 97.3%. The ResNet model achieves an accuracy of 94.3%, while AlexNet has an accuracy of 93.2%. In addition, the VGG12 model shows consistent performance with high precision, recall, and F1-Score values, making it a superior choice for medical applications. This study provides in-depth insights into the advantages and limitations of each CNN architecture, as well as implementation guidelines to support the development of image-based medical diagnosis applications efficiently and accurately.
IMPLEMENTASI APLIKASI WEBSITE SEKOLAH TAMAN KANAK-KANAK DEWI SARTIKA BANDUNG Lukman, Achmad; Fahlena, Hilda; Anuwiksa, I Wayan Palton
Jurnal AbdiMas Nusa Mandiri Vol. 7 No. 1 (2025): Periode April 2025
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/abdimas.v7i1.5946

Abstract

Community service activities held at Dewi Sartika Bandung Kindergarten School are based on several areas for improvement in publicizing the school's activities to the community. The school only had social media such as Instagram and Facebook during its establishment. Another shortcoming is the documentation of new student registration, which still uses registration forms, hard copies of family cards, and identity cards of prospective students' parents. The effectiveness of document storage could be improved. It is still prone to data loss, so it is necessary to have digital data storage to overcome these shortcomings by creating a website that is integrated with digital prospective student data collection. The method applied to measure success in this community service activity is quantitative method, namely giving a questionnaire which is then calculating to obtain a percentage value of success from this activity.  The results of community service activities carried out at the school are in the form of a website that can be accessed by teachers, parents of students, prospective school students, and public society and is expected to help with administrative work at the Dewi Sartika Bandung Kindergarten. The percentage of success of the website that has been created is in the “sufficient/normal” category, which is represented by a percentage value of 46.5 %.
Improving Performance Convolutional Neural Networks Using Modified Pooling Function Lukman, Achmad; Saputro, Wahju Tjahjo; Seniwati, Erni
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3763

Abstract

The Visual Geometry Group-16 (VGG16) network architecture, as part of the development of convolutional neural networks, has been popular among researchers in solving classification tasks, so in this paper, we investigated the number of layers to find better performance. In addition, we also proposed two pooling function techniques inspired by existing research on mixed pooling functions, namely Qmax and Qavg. The purpose of the research was to see the advantages of our method; we conducted several test scenarios, including comparing several modified network configurations based on VGG16 as a baseline and involving our pooling technique and existing pooling functions. Then, the results of the first scenario, we selected a network that can adapt well to our pooling technique, whichwas then carried out several tests involving the Cifar10, Cifar100, TinyImageNet, and Street View House Numbers (SVHN) datasets as benchmarks. In addition, we were also involved in several existing methods. The experiment results showed that Net-E has the highest performance, with 93.90% accuracy for Cifar10, 71.17% for Cifar100, and 52.84% for TinyImageNet. Still, the accuracy was low when the SVHN dataset was used. In addition, in comparison tests with several optimization algorithms using the Qavg pooling function, it can be seen that the best accuracy results lie in the SGD optimization algorithm, with 89.76% for Cifar10 and 89.06% for Cifar100.
Co-Authors Acihmah Sidauruk Ayu, Laras Agesti Eko Riswanto, Eko Faniasih, Rofi Fitrianie, Bella Haryoko, Haryoko Hilda Fahlena I Wayan Palton Anuwiksa Muhamad Irsan Salsabila, Nadia Saputri, Sekar Dewi Harnum Seniwati, Erni Syakur, Abdan Shofu Adlu Wahju Tjahjo Saputro Wahyudi, Abdal Rozaq Putra