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All Journal Teknosains: Media Informasi Sains dan Teknologi JIKO (Jurnal Informatika dan Komputer) JMM (Jurnal Masyarakat Mandiri) Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Jurnal Ilmiah Ilmu dan Teknologi Rekayasa Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Yayasan Cita Cendikiawan Al Khwarizmi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Informatika: Jurnal Pengembangan IT Jurnal Teknik Elektro
Muhammad Fikri Hidayattullah, Muhammad Fikri
Program Studi DIII Manajemen Informatika Politeknik Muhammadiyah Pekalongan
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Analisis Data Penjualan Pasca COVID-19 Menggunakan Algoritma K-Means Hapsari, Yustia; Hidayattullah, Muhammad Fikri; W, M Shidiq Erdi; Wibowo, Dega Surono; Nishom, M
Jurnal Informatika: Jurnal Pengembangan IT Vol 7, No 2 (2022)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v7i2.3810

Abstract

Pandemi Covid-19 memberikan dampak buruk ke berbagai sektor. Tak terkecuali sektor ekonomi. Bahkan negara-negara maju sekali pun ikut terkena dampak yang sangat serius di sektor ekonomi dari adanya pandemi ini. Para pelaku usaha dalam berbagai lini melakukan transformasi yang massif untuk bangkit dari keterpurukan tersebut. Salah satu langkah strategis yang dilakukan adalah dengan mengubah sistem marketing dan transaksi penjualan dari yang sebelumnya offline ke online. Penelitian ini akan melakukan analisis penjualan pasca Covid-19 dengan menggunakan dataset dari Narasio Data. Produk yang akan dianalisis berupa penjualan produk sport dan apparel. Hasil analisis akan menampilkan produk apa saja yang paling cepat terjual, pengaruh rating produk dan pemberian diskon dengan tingkat penjualan, serta keterkatian antara banyaknya variasi brand dengan tingkat penjualan. Proses analisis menggunakan pendekatan segemntasi produk. Algoritma yang digunakan untuk melakukan segmentasi adalah K-Means. Algoritma tersebut sangat handal untuk kasus clustering produk.
Studi Komparasi Algoritma SVM Dan Random Forest Pada Analisis Sentimen Komentar Youtube BTS Syafia, Anisa Nur; Hidayattullah, Muhammad Fikri; Suteddy, Wirmanto
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5064

Abstract

Sentiment analysis of YouTube boy group BTS comments uses the NLP approach to detect emotional patterns based on two category labels, namely positive and negative. With NLP, positive or negative polarity in an entity can be allocated as well as predicted high and low performance from various classification sentiments. The machine learning algorithms used to measure the accuracy of sentiment analysis developed are the Support Vector Machine and Random Forest algorithms. The steps taken start from the data collection obtained from the BTS YouTube Comment dataset and then go through the data preprocessing stage. Then proceed to the feature extraction stage by converting text into digital vectors or Bag of Words (BOW) and classified using machine learning algorithms until the evaluation stage. From the results comparison of the evaluated algorithms, the accuracy value between the two algorithms is 96% for training data and 85% for data testing using the SVM algorithm, while for the Random Forest algorithm it is 82% for training data and 80% for data testing. This shows that the SVM algorithm produces a higher accuracy value than the Random Forest for sentiment analysis of YouTube boy group BTS comments.
Sentimen Analisis Pandangan Masyarakat Terhadap Vaksinasi Covid 19 Menggunakan K-Nearest Neighbors Apriliani, Dyah; Susanto, Ardi; Hidayattullah, Muhammad Fikri; Sasmito, Ginanjar Wiro
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 1 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i1.4759

Abstract

Abstrak - Pandemi covid 19 yang terjadi sangat meresahkan masyarakat. Banyak masyarakat yang terpapar maupun kehilangan keluarga mereka karena virus ini. Untuk mencegah semakin menyebarnya virus covid 19, pemerintah menyelenggarakan program vaksinasi. Program vaksinasi yang dilakukan menuai pro dan kontra dari masyarakat. Berdasarkan permasalahan tersebut, maka dalam penelitian ini akan melakukan proses klasifikasi pandangan masyarakat terhadap vaksinasi Covid 19. Data penelitian yang digunakan diambil dari twitter sebanyak 2241 data. Data akan diklasifikasikan menjadi 2 kelas yaitu positif dan negatif. Proses klasifikasi akan dilakukan dengan menggunakan metode K-Nearest Neighbors (KNN).  Tahapan pertama yang dilakukan adalah pengambilan data dari twitter, pelabelan data, preprocesing data di phyton, pembobotan TF-IDF, pembuatan model, pengujian model dan evaluasi model. Dari penelitian ini didapatkan akurasi terbaik sebesar 79,25% dengan menggunakan parameter K-Fold 10 dan KNN 5.
Aplikasi Pemandu Wisata Berbasis Android Untuk 10 Wisata Bali Baru Firdaus, Musnadil; Wiyono, Slamet; Af'idah, Dwi Intan; Hidayattullah, Muhammad Fikri
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 1 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i1.6461

Abstract

Tourism has a very significant role in the Indonesian economy. In 2016, the Indonesian government prioritized 10 tourism destinations as "10 New Bali Tourism". However, tourists often face difficulties in finding accurate and efficient information about public facilities at tourist attractions. To overcome this problem, this research aims to develop an Android-based tourist guide application. This application is designed to help tourists find facility information quickly and accurately. This research was carried out through a series of stages, including design, analysis, design, implementation, testing and application maintenance. The result of this research is the development of a tour guide application that is useful for tourists. This application makes it easy for them to find accurate information about public facilities around the tourist attractions they visit. Unit testing of this application shows that no errors were found in the application functions. Usability test results were also very positive, with 88% of tourist users stating they were willing to use this application again in the future. This shows that this tour guide application has succeeded in providing an effective solution to the problems faced by tourists in searching for information on facilities at tourist attractions. Thus, this application can be a valuable tool in enhancing the tourist experience and supporting the growth of the Indonesian tourism industry. This research has produced an Android tourist guide application to facilitate travelers in finding public facilities at the "10 New Bali Tourism" destinations. With an 88% user satisfaction rate, the main contribution involves improving the traveler experience, supporting the growth of the tourism industry, and making a positive contribution to the Indonesian economy.
Cat Skin Disease Diagnosis Using EfficientNetV2 for Lightweight Processing on Low-Resource Devices Aminah, Fadila Rizka Nur; Mutasodirin, Mirza Alim; Hidayattullah, Muhammad Fikri
Jurnal Teknik Elektro Vol. 17 No. 2 (2025)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v17i2.29764

Abstract

Skin diseases are among the most common health issues in domestic cats. However, access to veterinarians is often limited, especially in low-resource settings. Automated image-based detection offers a fast and affordable alternative for early intervention. This paper presents a lightweight approach for diagnosing feline skin diseases using EfficientNetV2 optimized for low-resource devices. A balanced custom dataset consisting of 720 images across nine classes, namely Healthy, Mild/Severe Ringworm, Mild/Severe Acne, Mild/Severe Flea, and Mild/Severe Scabies, was compiled from Kaggle, Roboflow, and Google Images, ensuring ethical use of publicly available data. The images were augmented through rotations (0°, 90°, 180°, 270°) and horizontal flips, resulting in 5,760 images, to enhance model generalization. Five CNN architectures were benchmarked: DenseNet121, MobileNetV2, MobileNetV3, EfficientNetB0, and EfficientNetV2B0. Training was conducted with grid searches over batch sizes {64, 32, 16, 8} and learning rates {1e-3, 5e-4, 2e-4, 1e-4, 5e-5} for up to 300 epochs, and with the Adam optimizer and Reduce-LR-on-Plateau (decay factor 0.5). Early stopping (patience = 10) was used to mitigate overfitting. The best model was selected based on highest validation accuracy. The experiments were conducted on an Intel Xeon 6 CPU (2.2 GHz, 2 vCPUs) in Google Colab without GPU to simulate low-resource deployment. EfficientNetV2B0 achieved the best performance with 99.62% validation accuracy and 99.79% test accuracy, with an average inference latency of 78 ms/frame. Compared to previous studies focusing on heavyweight models or conventional ML using handcrafted features, this work highlights the feasibility of deploying an accurate real-time diagnostic pipeline on edge devices.
PEMANFAATAN APLIKASI GOOGLE LOOKER STUDIO UNTUK MEMBANTU VISUALISASI LAPORAN DATA KEUANGAN Hidayattullah, Muhammad Fikri; Handayani, Sharfina Febbi; Af'idah, Dwi Intan; Hapsari, Yustia
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 3 (2025): Juni
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i3.30285

Abstract

Abstrak: SMKN 1 Warureja menghadapi tantangan dalam meningkatkan pemahaman siswa tentang pelaporan dan analisis data keuangan, yang merupakan keterampilan penting di dunia kerja. Proses pelaporan saat ini masih menggunakan metode konvensional yang kurang menarik secara visual, sehingga mengakibatkan rendahnya minat siswa untuk belajar dan memahami. Salah satu solusi yang potensial adalah implementasi Google Looker Studio, sebuah platform visualisasi data interaktif yang dapat menyederhanakan informasi keuangan yang kompleks. Kegiatan Pengabdian Kepada Masyarakat ini bertujuan untuk meningkatkan hard skill berupa pengenalan aplikasi Google Looker Studio kepada siswa-siswi SMKN 1 Warureja untuk memahami konsep dan fungsi dasar dalam memvisualisasikan data khususnya dalam pelaporan keuangan. Kegiatan Pengabdian Masyarakat ini terdiri dari dua sesi utama, yaitu sesi penyampaian materi dan sesi workshop dengan praktik pembuatan visualisasi laporan keuangan menggunakan Google Looker Studio. Kegiatan ini diikuti oleh 20 orang peserta. Berdasarkan hasil evaluasi pre-test dan post-test dengan jumlah soal sebanyak 5 soal, diperoleh hasil bahwa terjadi peningkatan pengetahuan dan kemampuan teknis peserta mengenai penggunaan Google Looker Studio.Abstract: SMK Negeri 1 Warureja faces challenges in enhancing students' understanding of financial data reporting and analysis, which are essential skills in the professional world. The current reporting process still relies on conventional methods that lack visual appeal, leading to low student interest and comprehension. One potential solution is the implementation of Google Looker Studio, an interactive data visualization platform that can simplify complex financial information. This community service program aims to introduce Google Looker Studio to students at SMK Negeri 1 Warureja, enabling them to understand its fundamental concepts and functions in data visualization, particularly in financial reporting. The program consists of two main sessions: a theoretical session and a workshop session involving hands-on practice in creating financial report visualizations using Google Looker Studio. A total of 20 participants took part in this program. Based on the evaluation results from the pre-test and post-test, there was a noticeable improvement in participants' knowledge and technical skills in utilizing Google Looker Studio.
Perbandingan Akurasi dan Performa MobileNetV2 dan EfficientNetV2 Untuk Klasifikasi Sampah S Dharojat, Naufal Farros; Furqonsyah, M. Yugo Cahyo; Ghifary, Filamsi Mabda; Ardiyanto, Deny Faishal; Hidayattullah, Muhammad Fikri
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 14, No 1 (2026)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v14i1.89086

Abstract

Penelitian ini mengevaluasi kinerja model MobileNetV2 dan EfficientNetV2 untuk klasifikasi sampah menggunakan dataset gambar. Dataset yang diperoleh dari Kaggle terdiri dari 3.270 gambar yang dibagi menjadi lima kategori: botol kaca, botol plastik, gelas plastik, kaleng, dan kardus. Dataset ini dibagi menjadi set pelatihan (80%) dan pengujian (20%), dengan langkah-langkah pra-pemrosesan seperti pengubahan ukuran, normalisasi, dan augmentasi diterapkan untuk mengoptimalkan kinerja model. MobileNetV2, yang dirancang untuk efisiensi komputasi, mencapai akurasi 98% dengan kesalahan klasifikasi minimal, menunjukkan kinerja yang kuat dalam mengidentifikasi kategori sampah. Sebaliknya, EfficientNetV2 menunjukkan akurasi yang jauh lebih rendah, yaitu 27%, dengan tingkat kesalahan klasifikasi yang tinggi di semua kelas, mengindikasikan pelatihan yang tidak memadai dan potensi masalah optimasi. Metode evaluasi, termasuk akurasi, presisi, recall, dan F1-score, semakin menyoroti keunggulan kinerja MobileNetV2. Temuan ini menunjukkan bahwa MobileNetV2 lebih cocok untuk tugas klasifikasi sampah, terutama di lingkungan dengan keterbatasan sumber daya, sementara EfficientNetV2 memerlukan penyesuaian lebih lanjut untuk meningkatkan efektivitasnya.
Co-Authors Af'idah, Dwi Intan Af’idah, Dwi Intan Aminah, Fadila Rizka Nur Anantacia, Erryka Maura Adysti Ardi Susanto Ardiyanto, Deny Faishal Bimantoro, Elang Dairoh Dairoh, Dairoh Dega Surono Wibowo, Dega Surono Dyah Apriliani Fadilah, Bella Adha Firdaus, Musnadil Furqonsyah, M. Yugo Cahyo Ghifary, Filamsi Mabda Ginanjar Wiro Sasmito, Ginanjar Wiro Handayani, Sharfina Febbi Mubayyin, Meylin Tiflakhul Mutasodirin, Mirza Alim Nishom, M Pitaloka, Aura Putri , Novita Fitria S Dharojat, Naufal Farros Salsabila, Chaerunnisa Siti Khodijah Solikhin, Annur Riyadhus Syafia, Anisa Nur Taufiq Abidin, Taufiq W, M Shidiq Erdi Wirmanto Suteddy Wiyono, Slamet Yuliana, Ramanda Yustia Hapsari, Yustia