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All Journal Indonesian Journal of Information System Justek : Jurnal Sains Dan Teknologi STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Insearch: Information System Research Journal Indo-MathEdu Intellectuals Journal BEES: Bulletin of Electrical and Electronics Engineering Jutech: Jurnal Teknologi Informasi J-Icon : Jurnal Komputer dan Informatika Science and Technology: Jurnal Pengabdian Masyarakat Journal of Informatics and Information Security Smart Dedication: Jurnal Pengabdian Masyarakat Prosiding SeNTIK STI&K Technema: Journal of Intelligent Engineering and Computing
Indra Sari Kusuma Wardhana
Universitas Indraprasta PGRI
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Nursing Public Health Social Sciences Other

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Analisis Pengiriman Data Sensor dengan Jaringan Wireless Meggunakan Metode Quality of Service (QoS) Indra Sari Kusuma Wardhana; Bheta Agus Wardjiono
Justek : Jurnal Sains dan Teknologi Vol 5, No 2 (2022): November
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v%vi%i.11869

Abstract

Abstract:  Hydroponic plants are currently being cultivated, but on a large scale, the Internet of Things approach to hydroponic plant management systems can support and get optimal results. The existing management uses the GSM network to send sensor data to the web database and on some locations that are not covered by the GSM network are often found, thus experiencing problems in sending sensor data to the web database. The purpose of this study is to analyze the performance of a wireless network as an alternative to using GSM networks for sending sensor data, using the Quality of Service method in a hydroponic plant management system using Internet of Things technology. The research method used is to perform a simulation of testing data transmission on a wireless network with the help of the Wireshark application, while the calculations are assisted by Microsoft Excel. The results of the observations show that the performance of the wireless network is good and very good so that it can be used as a substitute for a GSM connection for sending sensor data to a web database.Abstrak: Tanaman hidroponik saat ini sedang marak dibudidayakan, namun untuk skala besar, pendekatan Internet of Things pada system pengelolaan tanaman hidroponik dapat mendukung dan mendapatkan hasil yang optimal. Pengelolaan yang ada menggunakan jaringan GSM untuk mengirimkan data hasil sensor ke webdatabase dan sering ditemukan lokasi yang tidak tercakup jaringan GSM, sehingga mengalami kendala dalam pengiriman data hasil sensor ke web database. Tujuan dari penelitian ini adalah menganalisis performa wireless network sebagai alternative penggunaan jaringan GSM untuk pengiriman data hasil sensor, dengan metode Quality of Service pada sistem pengelolaan tanaman hidroponik dengan menggunakan teknologi Internet of Things. Metode penelitian yang digunakan dengan melakukan simulasi pengujian pengiriman data pada wireless network dengan bantuan aplikasi Wireshark sedangkan perhitungannya dibantu dengan Microsoft Excel. Hasil dari pengamatan menunjukkan bahwa performa wireless network baik dan baik sekali sehingga dapat digunakan sebagai pengganti koneksi GSM untuk pengiriman data hasil sensor ke web database.
REAL-TIME STRUCTURAL ANALYSIS BASED ON MACHINE LEARNING FOR CUSTOM PRODUCT DESIGN: A CASE STUDY OF ORTHOPEDIC FIXATOR PRODUCT Aji Digdoyo; Adhitio Satyo Bayangkari Karno; Widi Hastomo; Agita Tunjungsari; Nada Kamilia; Indra Sari Kusuma Wardhana; Nia Yuningsih
J-ICON : Jurnal Komputer dan Informatika Vol 11 No 1 (2023): Maret 2023
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v11i1.9919

Abstract

Mass customization is related to increasing the balance between the needs of companies that are focused on customers on conditions of production flexibility and efficiency. Product adjustment according to customer needs can increase the company's competitiveness. However, special production processes and adjustments are time consuming and cost inefficient. Parametric product modeling is a fairly popular technique for dealing with this problem. However, it still has challenges related to the high cost of software and a workforce that has special expertise in the field of quality control. In addition, product-specific designs cannot be tested quickly, resulting in a long production time. This study proposes a machine learning (ML) method that aims to obtain a fast time structure to analyze the production of orthopedic fixators. This research process requires a collection of training data with product attributes, physical characteristics, quality, selected ML techniques, and determination of the appropriate set of hyperparameters. Optimization results were obtained using the gradient boosting method with a value of . With these results, the orthopedic fixation device can be used in the case study of developing this machine learning model.
Brain Tumor Classification Using Four Versions of EfficientNet Widi Hastomo; Adhitio Satyo Bayangkari Karno; Dody Arif; Indra Sari Kusuma Wardhana; Nada Kamilia; Rudy Yulianto; Aji Digdoyo; Tri Surawan
Insearch: Information System Research Journal Vol 3, No 01 (2023): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v3i01.5810

Abstract

Medical image processing approaches for detecting brain cancers are still primarily done manually, with low accuracy and taking a long period. Furthermore, this task can only be done by professionals with a high degree of medical competence, and the number of experts is obviously restricted in comparison to the large number of patients who need to be treated. With the growth of artificial intelligence and the rapid development of computers in terms of processing speed and storage capacity, it is feasible to assist doctors in classifying the existence of tumors in the head. This study employs four variations of the EfficientNet architecture to train a model on a variety of MRI imaging data. The model version B1 was shown to be the best in this investigation, with 98% accuracy, 99% precision, 95% recall, and 97% f1 score from versions B0 to B3 (4 versions). These results are excellent, but they do not rule out additional study utilizing various forms of design.
Identification of 29 Types of Plant Diseases using Deep Learning EfficientNetB3 Bayangkari Karno, Adhitio Satyo; Hastomo, Widi; Kusuma Wardhana, Indra Sari; Sutarno, Sutarno; Arif, Dodi
Insearch: Information System Research Journal Vol 2, No 02 (2022): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v2i02.4389

Abstract

To supply the world's food needs in the midst of the existing food crisis, farmers urgently need to expand crop production. By establishing it simple to recognize the kind of plant disease so that earlier control efforts could be conducted, farmers' harvest failures driven on by disease attacks must be prevented. In this study, one of the Convolutional Neural Network (CNN) architectures known EfficeintNetB3 is applied to generate a classification model for 29 different types of plant diseases. A model is created after 3,170 image data are used for validation and 57,067 image data were utilized for training. 3,171 image data tests were conducted as part of the model testing phase, and the total test results were produced an extraordinarily high accuracy score of 0.99 percentage and an F1-score
Perbandingan Dataset Labelled Faces in the Wild (LFW) dan faces94 Menggunakan Algoritma Convolutional Neural Networks (CNN) untuk Pengenalan Wajah Indra Sari Kusuma Wardhana; Widi Hastomo
Jurnal Teknologi Informasi (JUTECH) Vol 5 No 1 (2024): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v5i1.2584

Abstract

This research compares the performance of two popular datasets, Labelled Faces in the Wild (LFW) and faces94, in the task of face recognition using Convolutional Neural Networks (CNN) algorithms. The LFW dataset is known for its high variation in pose, lighting, and expression, while faces94 is more structured with more uniform lighting and pose conditions. CNNs were chosen for their ability to extract important features from face images for classification. In this study, a CNN model was trained on both datasets and its performance was evaluated using accuracy, precision, and recall metrics. The experimental results showed that the model trained on the faces94 dataset achieved higher accuracy compared to the model trained on the LFW dataset. However, the model on the LFW dataset demonstrated better resilience to variations in lighting and pose conditions. These findings indicate that while a more structured dataset like faces94 can produce a model with high accuracy under testing conditions similar to the training data, a dataset with greater variation like LFW is more suitable for real-world applications involving diverse conditions. This study provides important insights into the selection of datasets for developing robust and accurate face recognition systems.
Pelatihan Komputer Dasar dan Microsoft Office untuk Guru Pendidikan Usia Dini Wardhana , Indra Sari Kusuma; Putri , Basmallah Ramadhani Aisyah; Hastomo , Widi
Science and Technology: Jurnal Pengabdian Masyarakat Vol. 1 No. 3 (2024): September
Publisher : CV. Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/scitech.v1i3.152

Abstract

Artikel ini membahas pelaksanaan kegiatan pengabdian masyarakat melalui pelatihan komputer dasar dan Microsoft Office bagi guru Pendidikan Usia Dini (PAUD). Tujuan dari kegiatan ini adalah untuk meningkatkan keterampilan teknologi informasi guru-guru PAUD sehingga mereka dapat lebih efektif dalam mengelola administrasi dan mendukung proses pembelajaran. Pelatihan ini mencakup materi pengenalan komputer, penggunaan dasar Microsoft Word, Excel dan PowerPoint, serta penggunaan internet dan email, selain itu pengelolaan email menggunakan Microsoft Outlook. Hasil dari pelatihan menunjukkan peningkatan signifikan dalam keterampilan teknologi informasi bagi peserta.
DETEKSI COVID-19 IMAGE CHEST X-RAY DENGAN CONVOLUTION NEURAL NETWORK EFFICIENT NET-B7 Adhitio Satyo Bayangkari Karno; Dodi Arif; Indra Sari Kusuma Wardhana
Prosiding Seminar SeNTIK Vol. 5 No. 1 (2021): Prosiding SeNTIK 2021
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Di era pandemi keberadaan para medis dan rumah sakit terhadap jumlah pasien covid sangat tidak seimbang, lembaga kesehatan memerlukan alat bantu untuk tetap dapat memberikan pelayanan kesehatan. Kecerdasan buatan mampu memprediksi data image chest x-ray terhadap pasien penderita covid dan penyakit lainnya. Penelitian ini bermaksud untuk dapat mendeteksi covid-19 daridata image chest x-raymenggunakan Convolution Neural Network (CNN). Operasi yang ringandengan kualitas akurasi sangat baik dari arsitekturEfficienNet-B7 dapat dipergunakan oleh komputer performa tanpa Graphics Processing Unit (GPU).Dataset yang dipergunakan berbentukimage chest x-ray berjumlah 4.000 image, terdiri dari 4 klasifikasi yaitu covid, normal, lung opacity dan viral pneumonia masing-masing dengan jumlah data 1.000 image.Hasil penelitian dengan trainning 50 epoch diperoleh nilai akurasi trainning95,5% , akurasi validasi 91,8% dan akurasi testing 96%. Untuk tiap kelas hasil testing covid (96%) , normal (95%), lung opacity (93%) dan viral pneumonia (98%)
PENGOLAHAN BUDIDAYA TANAMAN HIDROPONIK DENGAN TEKNOLOGI INTERNET OF THINGS Indra Sari Kusuma Wardhana, Denny Boesrony dan Wishnu Kurniawan
Prosiding Seminar SeNTIK Vol. 4 No. 1 (2020): Prosiding SeNTIK 2020
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

PENGOLAHAN BUDIDAYA TANAMAN HIDROPONIK DENGAN TEKNOLOGI INTERNET OF THINGS
PEMANFAATAN INTERNET OF THINGS UNTUK CEGAH PENYEBARAN COVID-19 Indra Sari Kusuma Wardhana, Melani Dewi Lusita dan Diyah Ruri Irawati
Prosiding Seminar SeNTIK Vol. 4 No. 1 (2020): Prosiding SeNTIK 2020
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

PEMANFAATAN INTERNET OF THINGS UNTUK CEGAH PENYEBARAN COVID-19
PEMANFAATAN INTERNET OF THINGS PADA MONITORING PERFORMANCE APPRAISAL SYSTEM Hammam Huwaidi, , Mochammad Radja Brojas, Tb. M. Adrie Admira dan Indra Sari Kusuma Wardhana
Prosiding Seminar SeNTIK Vol. 4 No. 1 (2020): Prosiding SeNTIK 2020
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

PEMANFAATAN INTERNET OF THINGS PADA MONITORING PERFORMANCE APPRAISAL SYSTEM
Co-Authors Abdul Hakim Agita Tunjungsari Aji Digdoyo Aji Digdoyo Almurozy Mursidan Almurozy Mursidan Almurozy Mursidan Aryo Nur Utomo Basmallah Ramadhani Aisyah Putri Bayangkari Karno, Adhitio Satyo Bheta Agus Wardijono Deon Strydom Dodi Arif Dodi Arif Dody Arif Eka Sally Moreta Irawati, Diyah Ruri Linda Wahyu Widianti Nada Kamilia Nada Kamilia Nia Yuningsih Putri , Basmallah Ramadhani Aisyah Putri, Basmallah Ramadhani Aisyah Ramadhani Aisyah Putri, Basmallah Rudy Yulianto Sutarno Sutarno Tri Surawan Widi Hastomo