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All Journal Jurnal Simetris Kompak : Jurnal Ilmiah Komputerisasi Akuntansi E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Pixel : Jurnal Ilmiah Komputer Grafis Elkom: Jurnal Elektronika dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING Krea-TIF: Jurnal Teknik Informatika EDUMATIC: Jurnal Pendidikan Informatika EVOLUSI : Jurnal Sains dan Manajemen JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknologi Informasi dan Komunikasi Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Journal of Engineering, Electrical and Informatics (JEEI) Dinamika Jurnal Publikasi Ilmu Komputer dan Multimedia Jurnal Teknik Informatika dan Teknologi Informasi JURNAL AKUNTANSI DAN BISNIS Community: Jurnal Pengabdian Pada Masyarakat Journal of Technology Informatics and Engineering Transformasi Masyarakat : Jurnal Inovasi Sosial dan Pengabdian Scientific Journal of Informatics Switch: Jurnal Sains dan Teknologi Informasi Kesejahteraan Bersama : Jurnal Pengabdian dan Keberlanjutan Masyarakat Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial Pelayanan Unggulan: Jurnal Pengabdian Masyarakat Terapan Inovasi Sosial: Jurnal Pengabdian Masyarakat Journal of Engineering, Electrical and Informatics Jurnal Teknik Informatika dan Teknologi Informasi
Sindhu Rakasiwi
Universitas Sains Dan Teknologi Komputer
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Information System Supply Chain Management with FIFO Pertetual Method Kusumo, Haryo; Rakasiwi, Sindhu
Advance Sustainable Science, Engineering and Technology Vol 3, No 2 (2021): May-October
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v3i2.9722

Abstract

Inventory is one of the assets that has an important role for the activities of a company. Problems that often occur in inventory are at the time of recording and evaluating inventory. Generally, the recording is not detailed or even manual, thus making inventory reports unclear and not good. . This study applies an information system using Supply Chain Management (SCM) at PT. Von Mustika sejahtera, a company engaged in the distributor and retail of ornamental plants in the form of orchids. This system is built using the PHP programming language and MySQL database. This study resulted in an Accounting Information System using the FIFO Pertetual method which functions for inventory management and report presentation more effectively and efficiently.
Penerapan Model SVM dengan Ekstraksi Fitur ResNet50 untuk Identifikasi Sel Darah Terinfeksi Malaria Adhesyah Putra, Maulana Damar; Rakasiwi, Sindhu
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8750

Abstract

Malaria remains a major public health challenge in Indonesia, with 279,865 reported cases in 2023 and an Annual Parasite Incidence (API) of 0.99 per 1,000 population. Although microscopic examination is still considered the gold standard for malaria diagnosis, it has several limitations, including dependency on trained experts, subjective interpretation, and relatively lengthy processing time. To address these challenges, this study aims to analyze the performance of a Support Vector Machine (SVM) classifier with feature extraction based on ResNet50 in a Computer-Aided Diagnosis (CAD) system for automatic detection of malaria-infected blood cells.ResNet50 was selected for its transfer learning capability to generate high-level feature representations from medical images, while SVM was chosen due to its strong performance on high-dimensional data and limited datasets. A feature vector of 2048 dimensions produced from the global average pooling layer was classified using SVM with a Radial Basis Function (RBF) kernel. The dataset used in this study was obtained from the National Institutes of Health (NIH) and consists of 27,558 microscopic blood cell images (Parasitized and Uninfected classes). The data were partitioned using stratified sampling with an 80:20 ratio for training and testing. Preprocessing steps included pixel normalization, resizing to 224×224 pixels, and basic augmentation to improve model generalization. Experimental results show that the proposed model achieved an accuracy of 93.94%, precision of 94%, recall of 93.43% (Parasitized) and 94.46% (Uninfected), and an average F1-score of 94%. The confusion matrix indicates 2,575 true positives, 2,606 true negatives, 153 false positives, and 181 false negatives, with a false negative rate of 6.57% and a false positive rate of 5.54%. These findings demonstrate that the combination of ResNet50 and SVM has strong potential as a fast and accurate image-based malaria detection method and is suitable for implementation in healthcare settings with limited resources.
Pendekatan Ensemble Multi-Arsitektur Convolutional Neural Network melalui Soft Voting untuk Klasifikasi Citra Histopatologi Kanker Payudara Fitriyani, Shelomita; Rakasiwi, Sindhu
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8797

Abstract

Breast cancer is one of the leading causes of mortality among women, creating a strong need for diagnostic methods that are accurate, consistent, and capable of handling the morphological variations present in histopathological images. This study aims to improve the stability and accuracy of breast cancer histopathology image classification through an ensemble multi-architecture Convolutional Neural Network approach. The BreakHis dataset, which consists of four magnification levels 40×, 100×, 200×, and 400× was used in this research. Three architectures, VGG19, ResNet50, and EfficientNetB0, served as the base models. All images underwent preprocessing, including resizing to 224×224 pixels, pixel-intensity normalization, and data augmentation. Each model was trained independently, and their probability outputs were combined using a soft voting mechanism to generate the final predictions. The experimental results show that the ensemble method provides the most stable and superior performance across all magnification levels. At 40× magnification, the ensemble achieved an accuracy of 92.00%, recall of 99.03%, and F1-score of 94.44%. At 100× magnification, the accuracy increased to 94.56%, with a recall of 99.07% and an F1-score of 96.18%. The 200× level produced an accuracy of 94.03%, recall of 97.61%, and an F1-score of 95.77%. Meanwhile, at 400× magnification, the model reached an accuracy of 90.11%, recall of 95.14%, and an F1-score of 92.88%. These consistently high recall and F1-score values highlight the model’s strong ability to detect malignant cases while maintaining balanced predictive performance. Overall, the findings demonstrate that combining multiple CNN architectures enhances feature representation and shows strong potential as a decision-support system for breast cancer diagnosis using histopathological images.
SISTEM PRESENSI KARYAWAN MENGGUNAKAN RASPBERRY DENGAN SENSOR FINGERPRINT DAN WEBCAM Sindhu Rakasiwi; Haryo Kusumo; Agus Cahyo Pangestu
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 2 No. 2 (2022): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v2i2.372

Abstract

Proses presensi karyawan bagi sebuah perusahaan sangatlah penting untuk dilakukan. Karena presensi atau kehadiran karyawan menjadi tolak ukur untuk menentukan tingkat kedisiplinan seorang karyawan di sebuah perusahaan. Jika preseni dilakukan secara manual, maka akan memakan waktu yang cukup lama dan kurang efektif. Di PT Tunas Sahabat Tani proses masih di lakukan secara manual, proses pencatatan presensi yang manual ini dapat memicu para karyawan untuk melakukan titip absen. Dari masalah tersebut maka peniulis membuat “Sistem Presensi Karyawan Otomatis Menggunakan Raspberry Dengan Sensor Fingerprint Dan Webcam”. Dengan adanya sistem ini diharapkan dapat membatu proses kegiatan presensi karyawan di PT. Tunas Sahabat Tani sehingga pencatatan presensi menjadi lebih sistematis dan lebih terpantau dari pada sebelumnya. Pada sistem ini penulis menggunakan hardware raspberry pi sebagai pengendalinya dan sensor fingerprint untuk mendaftarkan dan menscan karyawan yang sedang melakukan kegiatan presensi serta menggunakan webcam untuk memotret para karyawan yang sedang melakukan kegiatan presensi, selanjutnya data akan disimpan di database dan staff HRD dapat mengakses web untuk melakukan repaitulasi kehadiran karyawan. Sistem yang di bangun menggunakan perangkat lunak PHP dan MYSQL. Dengan rekap yang sudah terkomputerisasi sehingga pencatatan karyawan lebih sistematis dan lebih terpantau
WAREHOUSE MANAGEMENT SYSTEM BERBASIS RADIO FREQUENCY IDENTIFICATION Moh Muthohir; Sindhu Rakasiwi; Lutfi Ubaidillah
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 3 No. 1 (2023): April: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v3i1.2139

Abstract

Minimarket Alfamart Kebondalem Kendal is one of the branch outlets of PT. Sumber Alfaria Trijaya Semarang where the management of the warehouse still uses conventional methods. Conventional management often creates difficulties in terms of the process of entering and leaving goods, knowing stock information, knowing the mutation of goods, and during the stock taking process. As a result of the difficulty in managing the warehouse, management cannot obtain warehouse information quickly and accurately. This makes warehouse productivity decrease because many items are not managed properly and disrupt operations. Based on the problems above, a prototype has been created that can help warehouse management by utilizing web-based RFID. The prototype system being developed will display goods data such as accurate stock, expiration date information, and history in out of goods which will be displayed on the web, so that stock-related information can be known quickly and the condition of goods in and out can be monitored at any time in real time
Optimalisasi Perilaku Hidup Bersih dan Sehat Melalui Aplikasi Kesehatan di SMP Ibu Kartini Subhiyakto, Egia Rosi; Rakasiwi, Sindhu; Dewi, Ika Novita; Zeniarja, Junta; Octaviani, Dhita Aulia; Salam, Abu; Fitriyani, Shelomita; Safira, Almira Zuhrotus
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3229

Abstract

Program Perilaku Hidup Bersih dan Sehat (PHBS) merupakan upaya penting dalam mendorong penerapan pola hidup sehat guna menjaga, merawat, serta meningkatkan derajat kesehatan. Penerapan gaya hidup sehat dapat mencegah berbagai penyakit yang berpotensi muncul di masyarakat. PHBS sangat tepat dikenalkan sejak usia sekolah, karena anak-anak termasuk kelompok yang rentan terhadap gangguan kesehatan akibat berbagai faktor. Perkembangan teknologi dalam bidang pendidikan telah terbukti mampu mengubah proses interaksi dan pembelajaran di kelas menjadi lebih efektif, efisien, mudah diakses, serta mendukung pengembangan keterampilan yang dibutuhkan di era digital, baik saat ini maupun di masa mendatang. Pemanfaatan aplikasi digital sebagai hasil perkembangan teknologi telah banyak diterapkan di bidang kesehatan dan pendidikan, yang keduanya saling berkaitan dan mendukung satu sama lain. Penyampaian informasi kesehatan membutuhkan peran pendidikan, sementara proses pendidikan juga tidak dapat berjalan optimal tanpa lingkungan yang sehat. Oleh karena itu, keberadaan teknologi dalam kedua bidang tersebut menjadi sangat krusial. Berdasarkan uraian tersebut, diperlukan pemberian pengetahuan mengenai PHBS kepada para siswa. Selain pemahaman secara teori, santri juga perlu mendapatkan pendampingan dalam penerapan PHBS secara langsung, serta dukungan teknologi berupa aplikasi digital agar proses pembelajaran menjadi lebih menarik dan efektif. Sebelum penerapan aplikasi tersebut, diperlukan sosialisasi dan pelatihan bagi pengasuh pondok pesantren terkait penggunaannya. Atas dasar pertimbangan tersebut, tim berinisiatif melaksanakan kegiatan Pengabdian Kepada Masyarakat dengan tema Pendampingan PHBS pada Siswa melalui Sosialisasi Aplikasi Digital yang berlokasi di SMP Ibu Kartini. Kegiatan ini diharapkan mampu membentuk kebiasaan PHBS dalam kehidupan sehari-hari santri serta mendorong mereka untuk menularkan perilaku positif tersebut kepada lingkungan sekitarnya.
Interpretable Ensemble Models for Lifestyle-Based Sleep Disorder Prediction Rahardian, Farhan; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12125

Abstract

Sleep disorders are a major global health concern that affect cognitive performance, mental well-being, and long-term physiological health. Conventional diagnostic methods such as polysomnography are time-consuming and resource-intensive, limiting their use for large-scale early detection. Therefore, machine learning offers a practical alternative for predictive and data-driven sleep disorder analysis. This study compares the performance of four ensemble learning algorithms Random Forest, Gradient Boosting, AdaBoost, and XGBoost in predicting sleep disorders based on lifestyle and physiological factors using the Sleep Health and Lifestyle dataset consisting of 374 samples and three classes: insomnia, none, and sleep apnea. The research workflow includes data preprocessing, feature encoding, dataset splitting (70:30), and hyperparameter optimization using Grid Search with 5-fold Cross Validation to improve model stability and generalization given the limited dataset size. Model evaluation is conducted using accuracy, precision, recall, and F1-score calculated with a macro-average approach to ensure fair multi-class performance assessment. The results show that AdaBoost and XGBoost achieve the highest test accuracy of 90.27%, while Random Forest and Gradient Boosting obtain 89.38%. Performance differences among models are relatively small (±1%) but indicate consistent predictive behavior. Feature importance analysis identifies BMI category and systolic blood pressure as the most influential predictors, followed by occupation and physical activity level, highlighting the relevance of lifestyle and physiological factors in sleep disorder risk. Overall, this study demonstrates that ensemble learning models provide reliable predictive performance and interpretable insights to support early detection of sleep disorders based on lifestyle patterns.
INFORMATION SYSTEM FOR INVENTORY OF CONSUMABLE GOODS AT SMP N 28 SEMARANG Andi Dharu Permana; Sindhu Rakasiwi
Journal of Engineering, Electrical and Informatics Vol. 1 No. 2 (2021): Juni: Journal of Engineering, Electrical and Informatics:
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v1i2.874

Abstract

Consumables inventory information system is used to control and supervise the management of consumables in carrying out the management of consumables, they often face problems. These problems such as inaccurate counting of goods, recording process, transaction data documentation is hampered, and inventory control is not optimal. The thing that is done to minimize these problems is the construction of an information system for managing consumables inventory. This system was created by developing an existing system. The purpose of developing this system is to provide convenience for users to obtain the required information quickly and accurately. The stages of system development used in the development of this system are system analysis, system design and system implementation. The auxiliary software used to implement the system design is Delphi Borland The consumables inventory information system is built based on user needs so that requirements specifications are generated. The database in this system includes a table of goods, users, suppliers, circulation, invoices, inventories, proposals for goods and expiration of goods.
Evaluasi Strategi Fine-Tuning pada ConvNeXt dan Swin Transformer untuk Klasifikasi Kanker Kulit Saputra, Ahmad Bintang; Rakasiwi, Sindhu
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9015

Abstract

Skin cancer is one of the diseases whose prevalence continues to increase every year, especially in areas with high exposure to ultraviolet (UV) rays. The main challenge in diagnosing skin cancer lies in the visual similarity between benign and malignant lesions, which often leads to misdiagnosis even by experienced medical personnel. The development of deep learning technology has made significant progress in medical image classification through a transfer learning approach. This study aims to compare the performance of two architectures from Transformer and CNN, namely Swin Transformer and ConvNeXt, in the task of classifying two class benign and malignant skin cancer images. Both models use pretrained from ImageNet and are applied with three different fine-tuning strategies, namely Linear Probe (LP), Full Fine-Tuning (FT), and a combination of the two previous strategies (LP-FT). The dataset used is the ISIC Archive Dataset with an 80:20 data split for training and validation, consisting of 3.297 images divided into two classes, with 1800 benign images and 1.497 malignant images. The evaluation was performed using the accuracy, precision, recall, and F1-score metrics. Swin Transformer with the LP-FT strategy achieved the best performance, with an accuracy of 92,27%, precision of 92,24%, recall of 92,17%, and an F1-score of 92,20%. These findings indicate that the two-stage fine-tuning approach can improve model stability and generalization, as well as contribute to the development of a more accurate artificial intelligence based skin cancer diagnosis system.
Analisis Komparatif Kinerja Algoritma Support Vector Machine, Random Forest, dan Naive Bayes untuk Klasifikasi Sentimen pada Komentar YouTube Dewantoro, Eustachius Dito; Rakasiwi, Sindhu
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8959

Abstract

The rise of social media platforms like YouTube has made them a primary medium for public discourse on socio-political issues, such as the "August 25th protests," which triggered massive polarization in the digital space. The vast volume of comments necessitates a computational approach for sentiment analysis. This study aims to classify public sentiment into positive and negative categories while comparing the performance of Naive Bayes, Random Forest, and Support Vector Machine (SVM). These algorithms were selected for their computational efficiency on high-dimensional text data compared to Deep Learning models. The methodology involved collecting 2,917 comments via the YouTube Data API v3, followed by text preprocessing, lexicon-based automated labeling, and TF-IDF feature weighting. To address the dataset's imbalance, where negative sentiment dominated at 78.8%, stratified sampling was applied to maintain class proportions. Results indicate that SVM achieved the highest accuracy at 88.2%, outperforming Random Forest (83.1%) and Naive Bayes (81.2%). SVM's superiority stems from its ability to find an optimal hyperplane that maximizes class margins, ensuring stability in imbalanced datasets. This research contributes a robust classification framework for understanding public opinion dynamics on specific political issues in Indonesia.
Co-Authors Abu Salam Adhesyah Putra, Maulana Damar Agus Cahyo Pangestu Agustinus Budi Santoso Albastomi, Taqius Shofi Andi Dharu Permana Andriana, Myra Arifin, Muhammad Farhan Ariyanto, Noval Arya Erlangga Astuti, Yani Parti budi hartono Cahaya Jatmiko Cahaya Jatmoko Cahyo Pangestu , Agus Candra Irawan Catur Supriyanto Daurat Sinaga Deddy Award Widya Laksana Dewantoro, Eustachius Dito Dewi Agustini Santoso Dzaky, Azmi Abiyyu Edi Sugiarto Edwin Zusrony Edy Mulyanto Egia Rosi Subhiyakto Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Erna Zuni Astuti Erna Zuni Astuti Erwin Yudi Hidayat Etika Kartikadarma Febryantahanuji Febryantahanuji Feri Agustina Fikri Budiman Fitriyani, Shelomita Guruh Fajar Shidik Haresta, Alif Agsakli Haryo Kusumo Haryo Kusumo Haryo Kusumo Heribertus Himawan Heru Lestiawan Ifan Rizqa Ika Novita Dewi Indra Laila Intan Nurul Alfiani Isnaini Khusnul Khotimah Jarot Dian Susatyono Jarot Dian Susatyono Jatmiko, Cahaya Junta Zeniarja Khani, Nadia Ifti Kurniawan, Defri Kusumo , Haryo Kusumo, Haryo Lalang Erawan Lalang Erawan Lutfi Ubaidillah Marjuni, Aris Moh Muthohir Mulyanto, Edy Munifah Murwoko, F Iwan Setyo Myra Andriana Norman, Maria Bernadette Chayeenee Nova Rijati Nur Rokhman Octaviani, Dhita Aulia Paramita, Cinantya Pulung Nurtantio Andono Putri, Chana Amelinda Rafsanjani, Muhammad Ivan Rahardian, Farhan Rifal Winazar Rifal Winazar Roymon Panjaitan Safira, Almira Zuhrotus Saputra, Ahmad Bintang Savicevic, Anamarija Jurcev Septiani, Karlina Dwi Shier Nee Saw Sinaga, Daurat Sri Wahyuning Suprapti suprayogi Suprayogi Suprayogi Syah Putra, Fernanda Mulya T.Sutojo Tantik Sumarlin . Taqius Shofi Albastomi Taufik Kurnialensya Triginandri, Rifqi Ubaidillah , Lutfi Utomo, Danang Wahyu Widya Laksana, Deddi Award Yani Parti Astuti Yuli Fitrianto