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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknisi Jurnal Sains Informatika Terapan (JSIT) Jurnal Pengabdian Masyarakat Dharma Andalas Jurnal Ekonomi, Manajemen Bisnis, Syariah dan Teknologi Jurnal Riset Multidisiplin dan Inovasi Teknologi The Indonesian Journal of Computer Science
nozomi, irohito
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Penerapan Data Mining Untuk Peringatan Dini Banjir Menggunakan Metode Klastering K-Means (Studi Kasus Kota Padang) Nozomi, Irohito
Jurnal Sains Informatika Terapan Vol. 2 No. 2 (2023): Jurnal Sains Informatika Terapan (Juni, 2023)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v2i2.165

Abstract

Bencana banjir sering terjadinya di wilayah Indonesia, salah satunya adalah Kota Padang. Dengan keadaan cuaca yang sering berubah, maka sulit memperdiksi apakah cuaca tersebut berpotensi banjir atau tidak. Sistem peringatan dini ini merupakan salah satu bentuk manajemen penanganan bencana yang bertujuan mengambil tindakan yang cepat dan tepat dalam memprediksi banjir dengan menggunakan teknik Data Mining. Penelitian ini merupakan proses untuk memprediksi bencana banjir dengan menggunakan metode Klastering K-Means. Data yang didapat dari BMKG dikelompokkan menjadi satu atau lebih cluster dan kemudian diolah. Analisis dan pengolahan data menggunakan tools RapidMiner v.5.3. Hasil dari penelitian ini didapatkan cluster yang menjadi tingkatan bahaya bencana banjir yang terdiri dari tingkatan rendah, tingkatan sedang, dan tingkatan tinggi.
Sistem Pendukung Keputusan Pemberian Kredit Nasabah BPR Guguk Mas Makmur Syahputra, Ronaldo; Nozomi, Irohito; Junaidi, Ahmad
Jurnal Riset Multidisiplin dan Inovasi Teknologi Том 2 № 01 (2024): Jurnal Riset Multidisiplin dan Inovasi Teknologi
Publisher : PT. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/jimat.v2i01.641

Abstract

One of the services provided by BPR Guguk Mas Makmur is providing credit to customers. The process of granting credit is carried out through various considerations and conditions that must be fulfilled by the customer, then the Bank considers whether the customer is worthy of taking credit or not. The process of granting credit at the BPR Guguk Mas Makmur branch takes a long time because it is still done manually, so customers often move to other financing companies. This excessively long time is caused by an inaccurate analysis process, so the assessment process must be repeated. This is considered very inefficient and ineffective. Therefore, it is necessary to create a decision support system for credit eligibility at BPR Guguk Mas Makmur.
Sosialisasi Pengenalan Formula Array pada Excel untuk Siswa Siswi SMA 1 Gunung Talang Solok Rahman, Sepsa Nur; Nozomi, Irohito; Surmayanti, Surmayanti
Jurnal Pengabdian Masyarakat Dharma Andalas Vol 2 No 2 (2024): Jurnal Pengabdian Masyarakat Dharma Andalas
Publisher : LPPM Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jpmda.v2i2.1235

Abstract

This Community Service aims to socialize the introduction of array formulas in Excel to SMA 1 Gunung Talang Solok students. Array formulas are a very useful feature in data processing in Excel, but are often not widely known by high school students. The method used in this research is a socialization method which involves presentations, demonstrations, and direct practice using array formulas in Excel. Socialization was carried out in the form of training involving SMA 1 Gunung Talang Solok students. The research results showed that the socialization of the introduction of array formulas in Excel had a positive impact on students at SMA 1 Gunung Talang Solok. After following the socialization, students were able to understand and apply array formulas in data processing in Excel. They are able to create array formulas that are more efficient and accurate in completing tasks that involve data processing
Comparison of Drug Type Classification Performance Using KNN Algorithm Aldi, Febri; Nozomi, Irohito; Soeheri, Soeheri
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11487

Abstract

The error of decommissioning is a serious problem that is often faced in medicine. In the face of these problems, information technology has a very important role. One of the information technologies that can be used is to use the machine learning classification algorithm K-Nearest Neighbor KNN. KNN is a type of machine learning algorithm that can be applied to problems with classification and regression prediction. The classification of types of drugs for patients greatly affects the health of the patient. The patient data is processed and transformed to numbers, which are then divided into training data and test data from 90:10, 80:20, 70:30 and using the Cross Validation model. KNN works through the nearest neighboring value with a value of k = 3 calculated by the calculation of Euclidean Distance, and then evaluated using the Confusion Matrix. The performance of the KNN algorithm resulted in the highest Accuracy value of 98.33%, a Precision value of 98.8%, a Recall value of 96.2%, and an F-measure value of 97.48%. The performance is obtained from the sharing of training data and 90:10 test data. The data share results in high performance compared to other data shares, including using the Cross Validation model. And the lower the k value, the higher the value of the resulting performance. The results show that the performance of the KNN algorithm is working well.
Machine Learning to Identify Monkey Pox Disease Aldi, Febri; Nozomi, Irohito; Sentosa, Rio Bayu; Junaidi, Ahmad
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12524

Abstract

In May 2022, it has received by WHO reports from non-endemic countries on cases of monkey pox disease. Monkey pox is a rare zoonotic disease caused by infection with the monkeypox virus that belongs to the genus orthopoxvirus and the family poxviridae, and also the variola virus. This study aims to classify patients who have contracted the monkey pox virus. We modeled an analysis of monkey pox disease and conducted comparisons utilizing a dataset from Kaggle consisting of a CSV file with records for 25,000 patients. The monkey pox dataset was analyzed using the correlation coefficient and the number of target variables. Machine learning (ML) methods are used for classification by utilizing the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) algorithms. This study resulted in the highest classifier Gradient Boosting (GB) algorithm with an accuracy value of 71%. then the accuracy obtained by Support Vector Machine (SVM) is 69%, Random Forest (RF) accuracy is 68%, and finally K-Nearest Neighbor (KNN) obtains 63% accuracy. This ML method is expected to analyze monkey pox disease so that it helps the country and government, especially the health field in assessing, identifying, and being able to take appropriate action against monkey pox disease.
SISTEM MANAJEMEN KEARSIPAN MENGGUNAKAN METODE WORK SYSTEM FRAMEWORK PADA BADAN PERPUSTAKAAN DAN KEARSIPAN Putra, Yeviki Maisyah; Syahputra, Ronaldo; Nozomi, Irohito
JURNAL TEKNISI Vol 4, No 2 (2024): Agustus 2024
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/teknisi.v4i2.2065

Abstract

Abstract:  Archives are often considered to have no effect on daily administrative activities and processes in every organization or agency, after there are letters or archives that are difficult to find or lost, then the leaders or managers realize that archives are the lifeblood of the agency, including the West Sumatra provincial library and archives agency whose main task is related to archiving all documents in the West Sumatra provincial government agency, because too many archives managed by the library and archives agency cause less than optimal performance in archiving documents and documenting archives and retrieval of old archives that take a long time to search. This study aims to develop an archive management system using the Work System Framework method at the West Sumatra Provincial Library and Archives Agency. The research methods used include needs analysis, system design, implementation, and evaluation. The results of the study indicate that the system developed can improve the efficiency of archive management and facilitate access to information. The conclusion of this study shows that the Work System Framework method is effective in developing a better archive management system.Keywords: archives management system; work system framework; archives. Abstrak: Arsip sering kali dianggap tidak berpengaruh terhadap kegiatan dan proses administrasi sehari-hari disetiap organisasi atau instansi, setelah ada surat atau arsip yang sulit ditemukan atau hilang maka barulah pimpinan atau manajer sadar bahwa arsip merupakan urat nadi instansi, termasuk pada badan perpustakaan dan kearsipan propinsi sumatera barat yang tugas pokoknya berkaitan dengan mengarsipkan seluruh dokument yang ada diinstansi pemerintah propinsi sumatera barat, karna terlalu banyaknya arsip yang dikelola oleh badan perpustakaan dan kearsipan menyebabkan kurang optimalnya kinerja dalam mengarsipkan dokument dan pendokumentasian arsip serta pencarian kembali arsip yang sudah lama yang memakan waktu lama dalam pencariannya. Penelitian ini bertujuan untuk mengembangkan sistem manajemen kearsipan menggunakan metode Work System Framework di Badan Perpustakaan dan Kearsipan Provinsi Sumatera Barat. Metode penelitian yang digunakan meliputi analisis kebutuhan, desain sistem, implementasi, dan evaluasi. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan dapat meningkatkan efisiensi pengelolaan arsip dan mempermudah akses informasi. Kesimpulan penelitian ini menunjukkan bahwa metode Work System Framework efektif dalam pengembangan sistem manajemen kearsipan yang lebih baik.Kata Kunci : sistem manajement kearsipan; work system framework; arsip.
PREDIKSI PRODUKSI DAN PENJUALAN MENGGUNAKAN METODE FUZZY TSUKAMOTO Nozomi, Irohito; Saputra, Ade
Jurnal Ekonomi Manajemen Bisnis Syariah dan Teknologi Vol. 4 No. 1 (2025): Jurnal Ekonomi, Manajemen Bisnis, Syariah dan Teknologi
Publisher : Yayasan Azam Insan Cendikia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62833/embistek.v4i1.164

Abstract

Metode Fuzzy Tsukamoto digunakan dalam sistem ini karena mampu mengatasi ketidak pastian dan kompleksitas dalam prediksi produksi dan penjualan. Metode ini memanfaatkan aturan-aturan fuzzy untuk mengolah input berupa variabel-variabel yang memengaruhi produksi dan penjualan, seperti permintaan pelanggan, stok bahan baku, dan faktor-faktor lainnya. Hasil dari metode ini akan memberikan keluaran berupa prediksi produksi yang dapat membantu perusahaan dalam mengatur produksi secara optimal. . Pengguna dapat memasukkan data-data terkait permintaan pelanggan, stok bahan baku, serta faktor-faktor lain yang relevan. Setelah input diberikan, sistem akan menerapkan metode Fuzzy Tsukamoto untuk menghasilkan prediksi produksi yang kemudian dapat digunakan dalam perencanaan produksi. Selain itu, sistem juga dapat memberikan rekomendasi penjualan berdasarkan data historis penjualan dan faktor-faktor pasar terbaru.Selain itu, metode Fuzzy Tsukamoto yang digunakan juga dapat diadaptasi dalam kontek bisnis lain yang memerlukan prediksi berdasarkan input berupa data yang tidak pasti.
Comparative Analysis of YOLOv11 with Previous YOLO in the Detection of Human Bone Fractures Aldi, Febri; Nozomi, Irohito; Hafizh, M.; Novita, Triana
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6051

Abstract

Accurate and rapid detection of bone fractures is an important challenge in the medical world, particularly in the field of radiology. This study aims to analyze and compare the performance of the YOLOv11 model with several previous versions of YOLO, namely YOLOv5, YOLOv8, and YOLOv10 in the task of detecting human bone fractures on X-ray and MRI images. The dataset used is the Human Bone Fractures Multi-modal Image Dataset (HBFMID) which consists of 641 raw images (510 X-rays and 131 MRIs). The four models were trained using the HBFMID dataset that had gone through a manual augmentation and annotation process, then tested using evaluation metrics such as precision, recall, mAP50, and mAP50-95. The training results showed that YOLOv11 has the most stable and consistent loss curve, with a fast convergence process. In terms of evaluation, YOLOv11 recorded a precision of 99.87%, a recall of 100%, a 99.49% mAP50, and an 84.13% increase in the number of mAP-95s, which generally outperformed other models. In addition, the visual prediction results show that YOLOv11 can detect fracture areas with the right bounding box and a balanced confidence score, without showing symptoms of overconfidence or inconsistency. When compared to approaches from previous studies, YOLOv11 also showed a significant improvement in detection accuracy. Thus, YOLOv11 is rated as the most optimal and reliable model in deep learning-based automatic bone fracture detection. This model has great potential to be applied in medical diagnosis support systems to improve the efficiency and accuracy of digital fracture identification.
Identifikasi Kanker Darah pada Gambar Apusan Darah Perifer (PBS) Menggunakan Ekstraksi Fitur HSV Nozomi, Irohito; Aldi, Febri
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4177

Abstract

Blood cancer is a category of diseases that have an impact on the development and operation of blood cells. Due to the complexity and diversity of these diseases, proper diagnosis is required before starting treatment. Medical imaging techniques have undergone significant advances in recent years, especially in peripheral blood smear (PBS) image processing. The aim of this study was to uncover how important the extraction of PBS image features is for the diagnosis of blood cancer. Feature extraction is essential to detect anomalies in blood cells in terms of blood cancer detection. The method used is feature extraction based on hue and saturation values (HSV) and uses Machine Support Vector Machine (SVM) machine learning algorithms in classifying malignant and benign PBS images. PBS image data used in this study was 100 images, consisting of 50 malignant PBS images, and 50 benign PBS images. Through the application of HSV feature extraction techniques and PBS image analysis, SVM algorithms can uncover latent indicators of blood cancer and facilitate timely and precise diagnosis. With the SVM technique, classification accuracy can be achieved by 92%. These results demonstrate the potential effectiveness of this feature extraction method. Extraction of HSV features may alter the diagnosis of blood cancer with additional research and application in clinical settings.
Cervical Cancer Classification Using Multi-Directional GLCM Shape-Texture Features in LBC Surmayanti, Surmayanti; Nozomi, Irohito; Aldi, Febri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15318

Abstract

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Co-Authors Ade saputra Ahmad Junaidi Aldi, Febri Hafizh, M. Novita, Triana Putra, Yeviki Maisyah Rahman, Sepsa Nur Sentosa, Rio Bayu Soeheri, Soeheri Surmayanti, Surmayanti Syahputra, Ronaldo