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All Journal Format : Jurnal Imiah Teknik Informatika Jurnal Khatulistiwa Informatika Jurnal Pilar Nusa Mandiri Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Mantik INTI Nusa Mandiri Abditeknika - Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Informatika Teknologi dan Sains (Jinteks) Paradigma Jurnal Aruna Mengabdi (ARMI)
Imam Nawawi
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Optimization of the YOLOv7 Object Detection Algorithm for Estimating the Amount of Apple Harvest Verry Riyanto; Imam Nawawi; Ridwansyah Ridwansyah; Ganda Wijaya; Toto Haryanto
Paradigma Vol. 25 No. 1 (2023): March 2023 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v25i1.1809

Abstract

The increasing population consumed in high production and food needs for survival. Apples are one of the crop harvest products in Indonesia whose needs are increasing, because they are not only needed for human vitamins but can be used as hand fruit or a form of gratitude to those who receive the fruit. In the process of harvesting apples in agricultural land, harvesting is often found which is not feasible in the hands of consumers because it takes too long for apples to not be harvested when the condition of the fruit is feasible in maturity. Therefore, the authors approach this problem by processing the image results obtained to form a detection model, whether the apples are said to be feasible to be harvested immediately and from the image results it can also be calculated the number of fruits captured by the image model , feature enhancements Estimates on objects from this image model are expected to provide more timely harvest predictions in order to provide longer aging of apples and good fruit quality after reaching consumers
Pelatihan Google Spreadsheet Untuk Mempermudah Pekerjaan Bagi PKK Kelurahan Paledang Risa Wati; Ahmad Fauzi; Imam Nawawi; Hilda Rachmi; Silvy Nur Azizah
Jurnal Aruna Mengabdi Vol. 1 No. 1 (2023): Periode Mei 2023
Publisher : Lotus Aruna Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61398/armi.v1i1.4

Abstract

Di Kecamatan Bogor Tengah, Kota Bogor terdapat Kelurahan Paledang. Salah satu kegiatan kelompok Ibu-ibu PKK di Kelurahan Paledang adalah melakukan sensus terhadap jenis pekerjaan seluruh warga Kelurahan Paledang. Pengolahan data masih dilakukan secara manual yang memakan waktu lama dan kurang efisien. Oleh karena itu, diperlukan kegiatan Pengabdian kepada Masyarakat. Kegiatan ini bertujuan untuk membantu kelompok Ibu-ibu PKK di Kelurahan Paledang dalam pengolahan data menggunakan Microsoft Excel melalui Google Spreadsheet melalui media handphone, sehingga pengolahan data dapat dilakukan oleh siapa saja, kapan saja, dan di mana saja, sambil tetap menjaga kerahasiaan informasi. Pengabdian masyarakat dilaksanakan dengan cara penyampaian materi secara langsung dan memberikan pelatihan dalam penggunaan Google Spreadsheet
SELEKSI ATRIBUT PADA DATA TIDAK SEIMBANG NASABAH KOPERASI DENGAN OPTIMASI SMOTE DAN ADABOOST Richky Faizal Amir; Andreyestha; Imam Nawawi; Andi Taufik; Eko Pramono; Fajar Akbar
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 5 No 3 (2023): EDISI 17
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v5i3.2757

Abstract

The credit procedure is the provision of credit on the basis of the bank's belief in the ability and ability of the customer to repay. In this study, the customer data tested was divided into 3 classes, namely 39 LOW customers, 84 MEDIUM customers, and 11 HIGH customers. Unbalanced datasets are pre-processed using the Synthetic Minority Over-sampling (SMOTE) technique. Classification methods such as Random Forest and Support Vector Machine will test cooperative customer data. The data is tested based on the attribute that has the highest matching value using the Particle Swarm Optimization method, this test is also optimized with the Adaboost method to increase its accuracy. The results of tests carried out using the Random Forest method obtained an accuracy of 89.05% and the Support Vector Machine algorithm obtained an accuracy of 81.75%. Meanwhile, testing with two methods optimized with Adaboost showed an increase in accuracy with Random Forest getting 91.24% accuracy and Support Vector Machine getting 82.48% accuracy. The highest accuracy in the cooperative customer data classification test was obtained from the Random Forest algorithm which was optimized with Adaboost at 91.24%
Physical Violence Detection System to Prevent Student Mental Health Disorders Based on Deep Learning Sukmawati Anggraeni Putri; Achmad Rifai; Imam Nawawi
Jurnal Pilar Nusa Mandiri Vol 19 No 2 (2023): Publishing Period for September 2023
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v19i2.4600

Abstract

Physical violence in the educational environment by students often occurs and leads to criminal acts. Apart from that, repeated acts of physical violence can be considered non-verbal bullying. This bullying can hurt the victim, causing physical disorders, mental health, impaired social relationships and decreased academic performance. However, monitoring activities against acts of violence currently being carried out have weaknesses, namely weak supervision by the school. A deep Learning-based physical violence detection system, namely LSTM Network, is the solution to this problem. In this research, we develop a Convolutional Neural Network to detect acts of violence. Convolutional Neural Network extracts features at the frame level from videos. At the frame level, the feature uses long short-term memory in the convolutional gate. Convolutional Neural Networks and convolutional short-term memory can capture local spatio-temporal features, enabling local video motion analysis. The performance of the proposed feature extraction pipeline is evaluated on standard benchmark datasets in terms of recognition accuracy. A comparison of the results obtained with state-of-the-art techniques reveals the promising capabilities of the proposed method for recognising violent videos. The model that has been trained and tested will be integrated into a violence detection system, which can provide ease and speed in detecting acts of violence that occur in the school environment.
OPTIMISASI PEMILIHAN FITUR UNTUK PREDIKSI GAGAL JANTUNG: FUSION RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION Imam Nawawi
INTI Nusa Mandiri Vol 18 No 2 (2024): INTI Periode Februari 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i2.5031

Abstract

Heart failure is a serious, life-threatening cardiovascular disease that increases with age and unhealthy lifestyles. Early prediction is essential to provide timely treatment and reduce mortality. The use of machine learning techniques, especially the Random forest (RF) method, for predicting heart failure has been previously researched, so the problem that occurs is that the RF method does not have maximum results because of irrelevant features. Selection of relevant features is a key step in building an accurate prediction model. Particle Swarm Optimization (PSO) is used to improve feature selection by searching for optimal combinations. The aim of the research is to reduce the mortality rate by improving the RF method with relevant features so as to increase the accuracy of predictions with Fusion RF and PSO. The results show an increase in accuracy of 02.78% to 87.33% with PSO, although the AUC decreased by 0.031%. The advantage of PSO is a significant increase in accuracy, but the disadvantage is a slight decrease in AUC. Future developments could explore how to address AUC degradation without compromising accuracy and transmitting additional relevant features.
ANALISIS PEMBAYARAN DIGITAL DANA DENGAN APLIKASI ISO/IEC 9126 MODEL BERDASARKAN FAKTOR KEGUNAAN Oky Kurniawan; Richky Faizal Amir; Andreyestha Andreyestha; Andi Taufik; Fajar Akbar; Imam Nawawi
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 6 No 1 (2024): EDISI 19
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v6i1.3418

Abstract

Pembayaran digital sudah menjadi tren untuk saat ini. Kemudahan penggunaan aplikasi dan biaya transaksi yang relatif murah telah mengubah pola perilaku masyarakat dalam melakukan pembayaran dan transaksi yang serba digital. Dana merupakan aplikasi dompet digital yang juga berfungsi sebagai pembayaran digital. Banyaknya pesaing saat ini khususnya di bidang pembayaran digital memerlukan adanya evaluasi terhadap kualitas perangkat lunak. Salah satu metode yang digunakan untuk mengukur kualitas perangkat lunak adalah ISO/IEC 9126, sebuah organisasi standardisasi internasional yang digunakan sebagai pedoman model kualitas. Dalam proses evaluasi Usability pada ISO/IEC 9126 terdapat karakteristik kualitas Usability yang meliputi sub-karakteristik kualitas yaitu: keterpahaman, kemampuan belajar, pengoperasian, dan daya tarik. Pengendalian kualitas perangkat lunak yang akan dievaluasi difokuskan pada perspektif Usability dengan tujuan untuk memuaskan kebutuhan pengguna. Nilai dikelompokkan berdasarkan tiga kategori, dari 0-100% tidak memuaskan antara 0%-40%, marginal antara 40%-60%, dan memuaskan antara 60%-100%. Dari hasil perhitungan nilai Usability sebesar 0,84 maka dapat dikatakan kategori memuaskan (satisfactory).
Co-Authors Abdillah Ari Abdul Hamid Achmad Rifai Ahmad Fauzi Ahmad Hafidzul Kahfi Akrom, Akrom Anak Agung Gede Sugianthara Andi Taufik Andi Taufik Andreyestha Andreyestha Andreyestha Eko Pramono Fajar Akbar Fajar Akbar Ganda Wijaya Ginabila Ginabila Hilda Rachmi Jamhari Jamhari Mochammad Abdul Azis Numan Musyaffa Nurajijah Nurajijah Oky Kurniawan Richky Faizal Amir Ricki Sastra Ridwansyah Ridwansyah Rino Ramadan Risa Wati - AMIK BSI Tasikmalaya Risa Wati - AMIK BSI Tasikmalaya Rizal Prasetyo Silvy Nur Azizah Siti Hani Nurlaela Sukmawati Anggraeni Putri Toto Haryanto Verry Riyanto Veti Apriana Widi Intan Priyanti