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All Journal Jurnal Sains Komputer dan Teknologi Informasi International Journal of Management Science and Information Technology (IJMSIT) Jurnal Atma Inovasia
Adiyah Mahiruna
Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Social Sciences Other

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ANALISIS PERFORMA INCEPTIONV3 CONVOLUTIONAL NETWORK PADA KLASIFIKASI VARIETAS DAUN GRAPEVINE: Performance Analysis of InceptionV3 Convolutional Network Used for Grapevine Leaves Varieties Classification Nurul Huda; Adiyah Mahiruna; Wellie Sulistijanti; Rina Chandra Noor Santi
Jurnal Sains Komputer dan Teknologi Informasi Vol. 5 No. 2 (2023): Jurnal Sains Komputer dan Teknologi Informasi
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

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Abstract

Daun Grapevine digunakan dalam berbagai masakan tradisional di seluruh dunia. Mengenali berbagai jenis daun Grapevine menjadi semakin penting karena harga dan rasanya bervariasi. Akan tetapi, identifikasi jenis daun ini secara manual akan sulit dan membutuhkan waktu yang lama. Sehingga, beberapa penelitian tentang klasifikasi daun ini dilakukan dengan memanfaatkan metode machine learning. Penelitian ini bertujuan untuk mengklasifikasikan 5 jenis daun Grapevine menggunakan arsitektur InceptionV3 yang merupakan salah satu arsitektur Convolutional Neural Network (CNN). Dataset yang digunakan adalah dataset publik yang terdiri dari 500 gambar, dimana untuk masing-masing kelas terdiri dari 100 gambar yaitu Ak (100), Ala Idris (100), Buzgulu (100), Dimnit (100), Nazli (100). Tahapan pertama dari penelitian ini dengan cara membagi dataset menjadi data training dan data testing. Prosentase data training sebesar 80% (400 gambar) dan data testing 20% (100 gambar). Tahapan selanjutnya dengan melakukan preprocessing gambar, dimulai dengan augmentasi gambar kemudian merubah ukuran gambar menjadi 300x300 pixel. Hasil dari preprocessing gambar inilah yang digunakan untuk uji coba model. Jika peneliti sebelumnya mengusulkan model berbasis Densenet-30 dan menghasilkan akurasi 98%, peneltian ini dengan menggunakan model InceptionV3 Convolutional Network berhasil mencapai akurasi sebesar 99.5%.
Klasterisasi Jurusan SMK Untuk Peningkatan Kerjasama Antara BLK Dengan SMK Di Blora Adiyah Mahiruna; Alya Masitha; Lathifatul Aulia; Arista Fitri Diana; Rowiyani; Alvin Muslikhun; Mukhlidin
Jurnal Atma Inovasia Vol. 6 No. 3 (2026)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jai.v6i3.12810

Abstract

The Minister of Primary and Secondary Education (Mendikdasmen), Abdul Mu'ti, emphasized the new policy direction for the development of Vocational High School (SMK) education. The Minister explained that future SMK graduates are expected to have not only diplomas, but also skills certificates that support their competitiveness and readiness in the world of work. The Ministry of Primary and Secondary Education (Kemendikdasmen) is collaborating with the Ministry of Manpower to establish Vocational Training Centers (BLK). The Community BLK Program is a government effort to increase the distribution of job training institutions. Higher Education Institutions (PT) in Indonesia are also given certain authorities to provide and organize study programs at various levels (diploma, bachelor's, master's, doctoral, specialist profession) according to the capabilities of each PT and industry needs. The ITESA Muhammadiyah Semarang team of community service lecturers will bridge the competency clustering in SMK with the clustering of human resource needs in the industry.
Optimizing K-means Clustering with Seed Initialization for Osteoporosis Diagnosis Based on Family History Adiyah Mahiruna; Ngatimin Ngatimin; Rachmat Destriana
International Journal of Management Science and Information Technology Vol. 6 No. 1 (2026): January - June 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i1.6648

Abstract

World Osteoporosis Day (WOD) is celebrated on October 20 every year, to raise global awareness about the prevention, diagnosis, and treatment of osteoporosis. Urgency in Indonesia, the number of elderly people is projected to reach 71 million people in 2050, which will have an impact on increasing cases of osteoporosis. Therefore, the recommendations based on scientific evidence in this study aim to assist practitioners in preventing osteoporosis in adults and children. This study proposes a method of Improving K-Means Performance through Seeds. The performance of the K-Means clustering algorithm is highly dependent on the random selection of initial centroids, which can lead to unstable clusters, suboptimal local solutions, and increased iterations, particularly in medical datasets such as osteoporosis diagnosis based on family history. Therefore, there is a need for an optimized centroid initialization strategy that can improve clustering accuracy and stability without increasing computational complexity. The dataset used is the osteoporosis dataset as a testing dataset that can be accessed publicly Osteoporosis dataset. The novelty of this study lies in the introduction of Modified Average (MA) approach for centroid initialization, which eliminates random seed dependency and improves clustering stability without increasing computational complexity. From the results of nine experiments with the benchmarking dataset, it can be seen that the method proposed in this study indicates that practically the Proposed method has a tendency to perform better in Rand Index measurement compare to k-means in random seeds.
Co-Authors Alvin Muslikhun ALYA MASITHA Arista Fitri Diana Destriana, Rachmat Lathifatul Aulia Mukhlidin Ngatimin Ngatimin Nurul Huda Rina Chandra Noor Santi Rowiyani Wellie Sulistijanti