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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pengabdian Masyarakat Bumi Raflesia International Journal of Social Service and Research Scientific Journal of Engineering Research Jurnal Informatika
Muis, Alwas
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Medical Image Classification of Brain Tumors using Convolutional Neural Network Algorithm Muis, Alwas; Sunardi, Sunardi; Yudhana, Anton
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6939

Abstract

Brain tumor is a highly dangerous and deadly disease. It can occur due to the abnormal growth of cells or tissues in the head. Treatment for brain tumor is done with surgery and chemotherapy aimed at killing or destroying the cells that affect the growth process of brain tumor. Diagnosis of brain tumor is done using medical scans such as MRI, CT Scan, and PET Scan by analyzing the resulting images. Another method used to detect brain tumors is through biopsy, which is a process of taking cells or tissue from the body for examination in the laboratory. However, this method takes a long time because the cells taken from the patient will be examined in the laboratory. Therefore, a technique is needed to speed up accurate brain tumor diagnosis in order to obtain quick treatment. Machine learning can solve this problem with the classification of images produced by MRI. The classification technique that can be used is the GoogLeNet architecture in CNN. Because GoogLeNet is the algorithm that won the ImageNet Large Scale Visual Recognition Challenge (ILSVC) in 2014 The purpose of this study is to classify brain images using the GoogLeNet architecture. The dataset used in this study consists of 7023 images, consisting of 6320 images for training the model and 703 for testing the model. The results of this study obtained an accuracy percentage of 96%. This result is higher than previous studies that obtained an accuracy value of 94%.
PUBLIC STIGMA ABOUT POLYGAMY BASED ON ISLAMIC-MUHAMMADIYAH VIEWS USING SENTIMENT ANALYSIS APPROACH Arqam, Mhd Lailan; Firdaus, Asno Azzawagama; Palahuddin, Palahuddin; Furizal, Furizal; Muis, Alwas; Atmojo, Ahmad Muslih
International Journal of Social Service and Research Vol. 4 No. 8 (2024): International Journal of Social Service and Research
Publisher : Ridwan Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/ijssr.v4i8.896

Abstract

Social media is very important to control the development of issues that occur today. With social shifts and changing societal values, polygamy has become a complex issue and attracts the attention of many people around the world discussed through social media platforms. This research contributes to the field by applying a sentiment analysis approach to automatically detect and analyze public sentiment regarding polygamiy content on Twitter, particularly in the context of Islamic-Muhammadiyah views. This study used decision tree classification methods, support vector machines, and random forests with the best analysis accuracy obtained at SVM 77.4%. Furthermore, the results of the sentiment class obtained were analyzed according to the views of Muhammadiyah. The results obtained in the analysis 77% commented negatively and 23% commented positively. In addition, this research can be used as a reference for future research on sentiment analysis cases to training and testing classroom models.
Digitalisasi Portofolio Siswa Berbasis Website di SMK Informatika Wonosobo Riadi, Imam; Umar, Rusydi; Muis, Alwas; Yunus, Muhajir
Jurnal Pengabdian Masyarakat Bumi Rafflesia Vol. 6 No. 3 (2023): Desember : Jurnal Pengabdian Kepada Masyarakat Bumi Raflesia
Publisher : Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Siswa dilatih untuk memiliki skill dan pengetahuan agar dapat bekerja pada industri. Setiap siswa dituntut memiliki pengetahuan yang dapat digunakan untuk mencari kerja setelah lulus. Kebutuhan akan adaptasi terhadap perkembangan teknologi informasi dan komunikasi dalam dunia pendidikan semakin meningkat. Sehingga dibutuhkan pelatihan terkait penggunaan teknologi informasi untuk membantu siswa beradaptasi dengan perkembangan teknologi. Tujuan pelatihan ini yaitu untuk memperkenalkan siswa tentang konsep portofolio digital berbasis website dan membekali mereka dengan keterampilan menggunakan teknologi informasi yang relevan untuk membangun dan mengelola portofolio. Manfaat pelatihan ini yaitu membantu siswa untuk mempresentasikan karya-karya mereka secara efektif kepada pihak-pihak yang berkepentingan seperti calon perguruan tinggi dan pemberi kerja. Metode analisis data pada pelatihan ini menggunakan metode likert dengan memberikan pernyataan dan memberikan jawaban mulai dari sangat setuju, setuju, netral, tidak setuju, dan sangat tidak setuju. Siswa berhasil membangun portofolio digital yang menarik dan profesional, memamerkan karya-karya mereka dengan efektif. Metode pengumpulan data pada kegiatan ini yaitu menggunakan kuesioner. ini menunjukkan bahwa pelatihan portofolio menggunakan website sangat mudah dipahami oleh siswa. Selain itu, seluruh siswa berharap pelatihan seperti sering diadakan untuk membantu dalam memanfaatkan teknologi informasi untuk pengembangan diri. Penerapan pelatihan ini di SMK Informatika Wonosobo memberikan manfaat yang signifikan bagi siswa dalam menghadapi tantangan di era digital.   Kata Kunci: Google sites, pelatihan, portofolio, website
Early Detection of Brain Tumors: Performance Evaluation of AlexNet and GoogleNet on Different Medical Image Resolutions Muis, Alwas; Rustiawan, Angga; Oyeyemi, Babatunde Bamidele; Syukur, Abdul; Furizal
Scientific Journal of Engineering Research Vol. 1 No. 3 (2025): September
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i3.2025.10

Abstract

Early detection of brain tumors through medical imaging is crucial to improving treatment success rates. This study aims to classify brain tumors using two deep learning models, AlexNet and GoogleNet, by testing three image sizes. The dataset used consists of four classes: glioma, no tumor, meningioma, and pituitary. The test results show that the AlexNet model achieves the best accuracy of 98% at a resolution of 150x150, while GoogleNet shows stable performance with the highest accuracy of 96% at both 150x150 and 200x200 resolutions. The medium resolution (150x150) proves to be optimal for both models, providing the best balance between visual information and processing efficiency. This study highlights the potential use of AlexNet and GoogleNet in brain tumor classification, with opportunities for performance improvement through further development, such as ensemble techniques and the use of a larger dataset.
PELATIHAN DESAIN UI/UX MENGGUNAKAN FIGMA UNTUK MEMBANGUN KETERAMPILAN KREATIF SISWA SMK NEGERI 1 KENDARI Yulanda, Fika; Aulia Nur Rahmatia Mumek, Putri; Nur Rahmatia, Mili; Ningsih, Nurna; Surtin; Muis, Alwas
Jurnal Pengabdian Masyarakat Bumi Rafflesia Vol. 9 No. 1 (2026): APRIL: Jurnal Pengabdian Kepada Masyarakat Bumi Raflesia
Publisher : Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/bumir.v9i1.10163

Abstract

Arus digitalisasi saat ini mengharuskan para lulusan sekolah menengah kejuruan memiliki kemahiran dalam desain digital relevan dengan kriteria industri modern, khususnya pada bidang User Interface (UI) serta User Experience (UX). Menanggapi kondisi tersebut, inisiatif pengabdian masyarakat ini diorientasikan untuk mengasah daya kreatif peserta didik lewat workshop perancangan UI/UX memakai platform figma di SMK Negeri 1 Kendari. Studi ini menerapkan strategi kuantitatif dengan desain pre-test dan post-test guna mengukur efektivitas peningkatan kompetensi siswa. Sebanyak 30 pelajar kelas X jurusan Teknik Jaringan Komputer dan Telekomunikasi (TJKT) dilibatkan sebagai partisipan. Rangkaian program mencakup fase persiapan, tingkatan aksi, hingga tahap penilaian. Fokus materi ditekankan pada teori dasar UI/UX, eksplorasi alat pada figma, hingga simulasi pembuatan prototipe yang interaktif. Teknik perolehan data mengandalkan angket berskala Likert yang sudah divalidasi dan diuji keandalannya secara ilmiah, dengan proses pengolahan data memakai statistik deskriptif serta uji t berpasangan. Capaian program ini mengonfirmasi adanya lonjakan kompetensi kreatif yang bermakna pada diri peserta didik. Hal ini terbukti dari skor rata-rata yang semula hanya 63,47 saat pengujian awal, melonjak menjadi 86,23 pada evaluasi akhir. Analisis statistik pun memperkuat temuan tersebut dengan angka signifikan p < 0,001, yang menegaskan bahwa edukasi UI/UX melalui platform figma berhasil mengoptimalkan wawasan teoretis, kemahiran teknis, dan daya cipta siswa dalam menyusun desain digital. Maka dari itu, inisiatif pengabdian ini berdampak positif bagi kemajuan pembelajaran berbasis keahlian pada Pendidikan vokasi melalui penggabungan teknologi desain digital.Kata Kunci: Desain UI/UX, pelatihan Figma, keterampilan kreatif, pendidikan vokasi, desain digital.
Comparative Analysis of Naïve Bayes Variants for Predicting Stunting-Risk Families Muis, Alwas; Razilu, Zila; Senga, Umy Ramadhani; Wulandari, Hutri; Andriyani, Reva
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v13i1.11910

Abstract

Stunting is a chronic nutritional condition that adversely affects children’s physical growth and cognitive development, highlighting the need for effective early detection, particularly at the household level. This study proposes a comparative analysis of three Naïve Bayes variants Gaussian, Multinomial, and Bernoulli to identify families at risk of stunting using machine learning techniques. The dataset used in this study consists of family-level records obtained from the National Population and Family Planning Agency (BKKBN) of Southeast Sulawesi Province, comprising demographic, socioeconomic, and health-related attributes. Data preprocessing involved handling missing values, removing irrelevant attributes, and transforming categorical variables. The dataset was divided into training and testing sets using an 80:20 ratio. The main contribution of this study lies in evaluating the effectiveness of different Naïve Bayes variants for family-based stunting risk prediction, which has been rarely explored in previous studies. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results indicate that the Bernoulli Naïve Bayes model achieved the best performance, with an accuracy of 88% and balanced evaluation metrics across both classes. These findings suggest that the Bernoulli Naïve Bayes model is the most suitable approach for predicting family-level stunting risk and can support data-driven early intervention strategies.
Co-Authors Abdul Syukur Andriyani, Reva Anton Yudhana Arqam, Mhd Lailan Asno Azzawagama Firdaus Atmojo, Ahmad Muslih Aulia Nur Rahmatia Mumek, Putri Furizal Furizal, Furizal Imam Riadi Muhajir Yunus Nur Rahmatia, Mili Nurna Ningsih Oyeyemi, Babatunde Bamidele Palahuddin, Palahuddin Razilu, Zila Rustiawan, Angga Rusydi Umar Senga, Umy Ramadhani Sunardi Sunardi Surtin Wulandari, Hutri Yulanda, Fika