Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal METIK JURNAL ABDIMASTEK JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Arwoko, Heru
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Klasifikasi Sampah Rumah Tangga Menggunakan Metode Convolutional Neural Network Sadida Aulia, Dini; Arwoko, Heru; Asmawati, Endah
METIK JURNAL Vol 8 No 2 (2024): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v8i2.956

Abstract

According to Law Number 18 of 2008, the increase in the volume, type and characteristics of waste is caused by population growth and changes in people's consumption patterns. Regulation of the Minister of Environment and Forestry of the Republic of Indonesia Number P.10/MENLHK/SETJEN/PLB.0/4/2018, concerning the handling of household waste and similar waste, it is necessary to classify it. In general, waste is divided into three types, namely Inorganic, Organic and B3. Many people already know the types of waste but are still not sure about sorting waste properly. It is necessary to recognize objects to design a household waste classification system to make it easier to sort waste more optimally. This system involves Deep Learning and in it there is an algorithm that is able to classify objects significantly, namely the Convolutional Neural Network (CNN) algorithm. This research uses the MobileNet architecture, which is a CNN architecture that has fast and accurate computing time. The dense layer contains 1 layer with 900 neurons. There are 5952 trash dataset images that will be used as training data and 1489 testing data taken from the kaggle.com dataset. The classification process for training data using the MobileNet architecture produces an average score for each class, namely an accuracy value of 88%, precision of 89%, recall of 88%, and F1 of 87%. Meanwhile, the results of the training model applied to the testing data produced an accuracy of 86%. Thus, the results of this experiment have quite good accuracy considering that household waste has various types and shapes.
Klasifikasi Sampah Rumah Tangga Menggunakan Metode Convolutional Neural Network Sadida Aulia, Dini; Arwoko, Heru; Asmawati, Endah
METIK JURNAL (AKREDITASI SINTA 3) Vol. 8 No. 2 (2024): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v8i2.956

Abstract

According to Law Number 18 of 2008, the increase in the volume, type and characteristics of waste is caused by population growth and changes in people's consumption patterns. Regulation of the Minister of Environment and Forestry of the Republic of Indonesia Number P.10/MENLHK/SETJEN/PLB.0/4/2018, concerning the handling of household waste and similar waste, it is necessary to classify it. In general, waste is divided into three types, namely Inorganic, Organic and B3. Many people already know the types of waste but are still not sure about sorting waste properly. It is necessary to recognize objects to design a household waste classification system to make it easier to sort waste more optimally. This system involves Deep Learning and in it there is an algorithm that is able to classify objects significantly, namely the Convolutional Neural Network (CNN) algorithm. This research uses the MobileNet architecture, which is a CNN architecture that has fast and accurate computing time. The dense layer contains 1 layer with 900 neurons. There are 5952 trash dataset images that will be used as training data and 1489 testing data taken from the kaggle.com dataset. The classification process for training data using the MobileNet architecture produces an average score for each class, namely an accuracy value of 88%, precision of 89%, recall of 88%, and F1 of 87%. Meanwhile, the results of the training model applied to the testing data produced an accuracy of 86%. Thus, the results of this experiment have quite good accuracy considering that household waste has various types and shapes.
Implementasi Game Berbasis Hand Gesture untuk Pelatihan dan Evaluasi Motorik Halus Anak di TK Yasmin Jember Arwoko, Heru; Siswantoro, Joko; Asmawti, Endah; Ariyani, Sofia
ABDIMASTEK Vol. 4 No. 1 (2025): Juli
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/abdimastek.v4i1.3554

Abstract

Kemampuan motorik halus merupakan aspek penting dalam perkembangan anak usia karena berperan dalam keterampilan dasar seperti menulis, menggambar, dan memegang objek kecil. Namun, metode pelatihan konvensional sering kali kurang menarik dan tidak menyediakan umpan balik objektif terhadap perkembangan anak. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan dan mengevaluasi keterampilan motorik halus anak melalui media game edukatif berbasis pengenalan gesture tangan (hand gesture) secara real-time. Program diterapkan di TK Yasmin Jember dengan melibatkan 32 anak usia 4–6 tahun. Game dirancang agar anak memindahkan objek virtual menggunakan gerakan tangan yang direkam dan dianalisis oleh sistem berbasis kamera. Data yang diperoleh diklasifikasikan ke dalam kategori performa (Baik, Cukup, Kurang) dan dibandingkan dengan penilaian guru. Hasil menunjukkan tingkat kesesuaian sebesar 93,75% antara klasifikasi sistem dan observasi manual guru. Selain peningkatan koordinasi tangan-mata dan fokus atensi, media ini juga mendapat tanggapan positif dari guru dan orang tua sebagai sarana pembelajaran inovatif. Temuan ini mengindikasikan bahwa pendekatan aplikasi berbasis gesture memiliki potensi sebagai alat bantu yang efektif, menyenangkan, dan objektif dalam mendukung perkembangan motorik anak usia dini.
Ultrasound Image Classification of Breast Cancer Using MobileNet Arwoko, Heru; Sofia Ariyani
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 1 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i1.4860

Abstract

Breast cancer is one of the most prevalent diseases affecting women and has a high mortality rate if not detected at an early stage. Therefore, the development of an automated and accurate system for breast cancer diagnosis is of critical importance. One of the most commonly used methods for early breast cancer detection is medical ultrasonography (US) imaging, as it is safe and easily accessible. However, ultrasound images suffer from several limitations, including low image quality, high noise levels, and heterogeneous characteristics, which make the classification of cancer types challenging. In this study, a transfer learning approach is employed for breast ultrasound image classification by utilizing the MobileNet architecture, which is lightweight and computationally efficient, to enhance model performance. The classification task is performed on three classes: benign tumors, malignant tumors, and normal tissue. The dataset used is the BUSI (Breast Ultrasound Images) dataset obtained from Baheya Hospital, Cairo, Egypt, consisting of 780 breast ultrasound images. Experiments are conducted using several pre-trained architectures, including MobileNet, MobileNetV2, Xception, and InceptionV3. The evaluation results demonstrate that the MobileNet architecture achieves the best performance with an F1-score of 89%. These results indicate that the proposed approach is effective for classifying ultrasound images, as features are automatically and globally learned by the neural network without requiring manual geometric feature analysis.
Co-Authors Asmawati, Endah Asmawti, Endah Joko Siswantoro Sadida Aulia, Dini Sofia Ariyani