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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Generic Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Klik - Jurnal Ilmu Komputer Jurnal Indonesia Sosial Sains ILTEK : Jurnal Teknologi Ekasakti Matua Jurnal Manajemen JT-IBSI (Jurnal Teknik Ibnu Sina) Jurnal Ekonomi dan Bisnis
Saputra, Tommy
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Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Neuroscience Physics Social Sciences

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Heart Chamber Segmentation in Cardiomegaly Conditions Using the CNN Method with U-Net Architecture Saputra, Tommy; Nurmaini, Siti; Roseno, Muhammad Taufik; Syaputra, Hadi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1976

Abstract

Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness of breath, abnormal heartbeat and edema. Cardiomegaly will cause the sufferer's heart to pump harder than usual. Early diagnosis of cardiomegaly can help make decisions about whether the heart is abnormal or normal. In addition, due to the problem that manual examination takes time and requires human interpretation and experience, tools are needed to automatically develop and identify normal and abnormal hearts. Therefore, this study proposes cardiac chamber segmentation using 2D (two-dimensional) ultrasound convolutional neural networks for rapid cardiomegaly screening in clinical applications based on heart ultrasound examination. The proposed approach uses a CNN with a U-Net architecture model with abnormal and normal heart data. The research results obtained used the pixel matrix evaluation Avg_accuracy of 99.50%, Val_accuracy of 97.98% and Mean_IoU of 90.01%.
Sistem Absensi Karyawan Menggunakan Self Potrait dan Geolocation Pada PT Sucofindo Palembang Saputra, Tommy; Utari, Aspirani; Teisnajaya, Usep; Twenty Agustine, Grace
Klik - Jurnal Ilmu Komputer Vol. 4 No. 2 (2023): Klik - Jurnal Ilmu Komputer
Publisher : Fakultas Ilmu Komputer Universitas Sumatera Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56869/klik.v4i2.553

Abstract

Penelitian yang telah dilakukan pada PT Sucofindo Palembang diperoleh masalah bahwa sistem absensi yang diterapkan saat ini masih menggunakan sistem manual, dimana proses absensi dilakukan dengan mencatat pada lembar kertas sehingga membutuhkan waktu yang lama dan boros penggunaan kertas. Rentan juga adanya manipulasi data atau terjadi data yang ganda, sehingga tidak mendapatkan hasil yang akurat. Tentunya hal tersebut membuat proses absensi menjadi kurang efektif. Salah satu dari solusi untuk mengatasi permasalahan di atas yaitu dengan membangun sebuah sistem yang terkomputerisasi untuk menggantikan metode manual dan membantu karyawan dalam proses absensi kehadiran di PT Sucofindo Palembang. Metode yang digunakan pada penelitian ini adalah metode waterfall secara terurut dimulai dari analisis, desain, pengkodean, pengujian, dan pemeliharaan. Proses desain rancangan sistem menggunakan UML (Unified Modeling Language) yang terdiri dari use case diagram, activity diagram, sequence diagram, class diagram, dan rancangan database. Hasil dari penelitian ini yaitu sebuah sistem absensi karyawan menggunakan self portrait dan geolocation berbasis web. Sistem ini dibuat agar karyawan bisa melakukan absensi dengan mudah menggunakan handphone dengan self portrait dan Geolocation, yang mana input absensi ini hanya bisa dilakukan di area PT Sucofindo Palembang saja sehingga mengurangi tingkat kecurangan dalam input absensi, karena dapat diakses menggunakan ponsel yang karyawan miliki, dan membantu Admin dalam merekap data absensi di PT Sucofindo Palembang
Faktor - Faktor Yang Menentukan Itensi Turnover Karyawan pada PT. Global Asia Seluler Sanusi; Saputra, Tommy; Hidayat, Hendri
Jurnal Teknik Ibnu Sina (JT-IBSI) Vol. 6 No. 01 (2021): Jurnal Teknik Ibnu Sina (JT-IBSI)
Publisher : Fakultas Teknik Universitas Ibnu Sina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jt-ibsi.v6i01.256

Abstract

Turnover dengan metode Fishbone dan PDCA adapun metode pengumpulan data dengan menggunakan metode kuesioner, dengan jumlah populasi 42 orang. dengan menggunakan metode kuesioner, dengan jumlah populasi 42 orang. Dari hasil pengamatan bahwa jenis persentase grup yang memiliki pengaruh terhadap Turnover adalah berdasarkan Stres Kerja dengan jumlah persentase, Target perusahaan dan tuntutan tugas terlalu tinggi 26%, atau memiliki 71 total akhir dan Peran yang saya terima di perusahaan ini sering bertentangan satu sama lain 25% dengan 68 total akhi.
Klasifikasi Kanker Payudara Menggunakan Metode Convolutional Neural Network (CNN) dengan Arsitektur VGG-16 Idawati, Idawati; Rini, Dian Palupi; Primanita, Anggina; Saputra, Tommy
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7553

Abstract

Breast cancer classification is a process to determine the type and characteristics of breast cancer based on the characteristics of cancer cells. In this research, a system is designed to classify breast cancer using ultrasound images which are then processed using the Convolutional Neural Network method with the VGG-16 architecture. The aim of the research is to develop a breast cancer classification system using Convolutional Neural Network (CNN) and evaluate the classification results using Convolutional Neural Network (CNN) with the VGG-16 architecture. In breast cancer classification, three classes are considered: normal, benign, and malignant. The steps in the classification process include image input, filtering, resizing, data augmentation, and data digitization. The best results were obtained in this test using the SGD optimizer hyperparameter, learning rate 0.001, epoch 20 and batch size 32 producing an accuracy value of 78.87%, a precision value of 75.69%, an AUC value of 79.85% and an f1 score value of 74.67%.
Pengaruh Kompetensi dan Penilaian Prestasi Kerja Terhadap Kinerja Guru SMA Pertiwi 2 Padang Saputra, Tommy; Yanti, Novi
Ekasakti Matua Jurnal Manajemen Vol. 1 No. 2 (2023): Ekasakti Matua Jurnal Manajemen (April 2023)
Publisher : Fakultas Ekonomi, Universitas Ekasaktii

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31933/emjm.v1i2.849

Abstract

Abstract: This study aims: (1) To examine the effect of competence partially on teacher performance at SMA Pertiwi 2 Padang; (2) To test the effect of partial performance appraisal on teacher performance at SMA Pertiwi 2 Padang. (3) to test the effect of competency and performance appraisal simultaneously on teacher performance at SMA Pertiwi 2 Padang. The technique in analyzing the data uses multiple linear regression, t-test, F test, and coefficient of determination. The test results show (1) the Competency Variable positively and significantly influences teacher performance at SMA Pertiwi 2 Padang Padang. (2) Performance appraisal also has a positive and significant effect on teacher performance at SMA Pertiwi 2 Padang (3) Competency and performance appraisal have a significant effect simultaneously on teacher performance at SMA Pertiwi 2 Padang with sig 0.000. The magnitude of the contribution of the influence of competency and performance appraisal on teacher performance at Pertiwi 2 Padang High School is 66.5% while the remaining 33.5% is contributed by other factors. Abstrak: Penelitian ini bertujuan: (1) Untuk menguji pengaruh kompetensi secara parsial terhadap kinerja guru SMA Pertiwi 2 Padang; (2) Untuk menguji pengaruh penilaian prestasi kerja secara parsial terhadap kinerja guru SMA Pertiwi 2 Padang. (3) untuk menguji Pengaruh kompetensi dan penilaian prestasi kerja secara simultan terhadap kinerja guru SMA Pertiwi 2 Padang. Teknik dalam menganalisisis datanya memakai regresi linier berganda, uji t, uji F serta koefisien determinan. Hasil pengujian memperlihatkan (1) Variabel Kompetensi positif dan signifikan memengaruhi kinerja guru SMA Pertiwi 2 Padang Padang. (2) Penilaian Prestasi Kerja juga berpengaruh positif dan signifikan terhadap Kinerja Guru SMA Pertiwi 2 Padang (3) Kompetensi dan penilaian prestasi kerja berpengaruh signifikan secara simultan pada kinerja guru SMA Pertiwi 2 Padang dengan sig 0,000. Besarnya kontribusi pengaruh kompetensi dan penilaian prestasi kerja terhadap kinerja guru SMA Pertiwi 2 Padang adalah sebesar 66,5% sedangkan sisanya 33,5% adalah kontribusi faktor yang lain.
Dilatasi Inkremental Menggunakan Metode CNN Untuk Klasifikasi Tumor Otak Dengan Arsitektur VCG16 dan Resnet50 Saputra, Tommy; Roseno, Muhammad Taufik; Syaputra, Hadi
Generic Vol 16 No 2 (2024): Vol 16, No 2 (2024)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/generic.v16i2.190

Abstract

Klasifikasi tumor otak adalah tugas yang menantang di bidang pemrosesan citra medis. Teknologi kini telah memungkinkan dokter medis untuk memiliki bantuan tambahan untuk diagnosis. Penelitian ini bertujuan untuk mengklasifikasikan tumor otak menggunakan gambar MRI, yang dikumpulkan dari pasien anonim dan simulator otak buatan. Baru-baru ini, teknik berbantuan komputer seperti menggunakan deep learning sebagai ekstraksi fitur, dan teknik klasifikasi digunakan secara intensif untuk mendiagnosis otak pasien untuk memeriksa apakah ada tumor. Dalam penelitian ini diusulkan model klasifikasi tumor otak menggunakan Convolutional Neural Network yang dapat menklasifikasikan tumor otak secara akurat. Data yang digunakan berupa data MRI tumor otak sebanyak 253 data tumor otak. Dataset yang dugankan dibagi menjadi data pelatihan dan pengujian. Penelitian menghasilkan model klasifikasi tumor otak dengan menggunakan arsitektur VCG16 dan Resnet50. Model menghasilkan nilai rata-rata akurasi sebesar 80%, Recall 85% dan Presisi 70%. Penelitian menunjukkan kinerja Resnet50 menunjukkan kemampuan model untuk mengklasifikasikan tumor otak secara akurat.
Implementasi CRM dengan Metode Crosselling dan Upselling Berbasis Web untuk Peningkatan Penyewaan Mobil pada Cahaya Rental Mobil Saputra, Tommy; Febriady, Mukhlis; Nurqolbiah, Fatihani; Ubaidillah, Ubaidillah; Pratomo, Yudha
Klik - Jurnal Ilmu Komputer Vol. 5 No. 2 (2024): Klik - Jurnal Ilmu Komputer
Publisher : Fakultas Ilmu Komputer Universitas Sumatera Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56869/klik.v5i2.618

Abstract

Perkembangan dunia usaha yang semakin maju menyebabkan timbulnya persaingan yang semakin ketat. Salah satu cara untuk menjaga kelangsungan hidup perusahaan adalah menjaga hubungan baik dengan pelanggan. Persaingan bisnis yang sangat ketat akan sangat memerlukan langkah-langkah strategis untuk menghadapinya. Adapun kondisi yang terdapat pada cahaya rental mobil antara lain pada proses pemasaran perusahaan masih belum memiliki media online untuk menyampaikan informasi produk yang akan diberikan kepada pelanggan. Dari hasil permasalahan tersebut, maka perlu dilakukan analisis yakni dengan cara melakukan analisa kebutuhan, pengguna dan fasilitas yang dibutuhkan oleh cahaya rental mobil . Berdasarkan hasil analisis permasalahan yang telah dilakukan sebelumnya, maka dapat dirancang suatu perangkat lunak dengan melakukan penerapan sistem customer relationship management dengan metode crosselling dan upselling berbasis web untuk meningkatkan hubungan baik dengan pelanggan dan dapat membantu dalam pengolahan data sewa serta menampilkan informasi mobil yang dibutuhkan oleh pelanggan.
Evaluating Usability and Clustering of SILCARE System for MSME Shipping: A Data-Driven Approach Using SUS and User Behavior Analysis Permatasari, Ririt Dwiputri; Bora, M Ansyar; Hernando, Luki; Saputra, Tommy; Fauzan, Haidil; Shilah, Nur; Salsabila, Tia Andini
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.590

Abstract

The SILCARE system is a digital logistics platform designed to optimize shipping operations for Micro, Small, and Medium Enterprises (MSMEs). This study evaluates its usability and user behavior patterns through System Usability Scale (SUS) assessments and clustering analysis. The research involved 100 SME users performing key system tasks such as registration, product management, and order confirmation. The SUS results showed a significant usability improvement, with the pre-test score of 74.5 (B grade) increasing to 90.25 (A grade) in the post-test, indicating enhanced user experience. User interaction data analysis revealed that registration took an average of 7.11 minutes, product addition 8.91 minutes, and order confirmation 5.15 minutes. Clustering using DBSCAN identified four distinct user groups, highlighting behavioral differences, where 37% of users struggled with complex tasks while 25% displayed balanced engagement. These findings inform targeted system improvements, such as simplifying workflows for new users and enhancing features for power users. The novelty of this study lies in integrating usability testing with behavior-driven clustering to refine a logistics platform tailored to MSMEs. By leveraging data-driven insights, the SILCARE system contributes to digital transformation in MSME logistics, improving operational efficiency and user satisfaction The paper explores the development process of the system, starting from the requirements gathering phase, where user needs were identified through extensive surveys and interviews with stakeholders. The iterative prototyping method allowed for the creation of an initial version of the system that was refined based on user feedback, ensuring that the final product met both functional and usability standards. The SILCARE system holds substantial promise for MSMEs, offering a digital solution for streamlining logistics and shipping processes and contributing to the overall success of small businesses.
Residual pixel-wise semantic segmentation for assessing enlarged fetal heart: a preliminary study Roseno, Muhammad Taufik; Nurmaini, Siti; Rini, Dian Palupi; Saputra, Tommy; Mirani, Putri; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Syaputra, Hadi
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9244

Abstract

The four-chamber view is a crucial scan plane routinely employed in both second-trimester perinatal screening and fetal echocardiographic examinations. Sonographers typically measure biometrics in this plane, such as the cardiothoracic ratio (CTR) and heart axis, to diagnose fetal heart anomalies. However, due to the echocardiographic artifacts, the assessment not only suffers from low efficiency but also inconsistent results depending on the operators’ skills. This study proposes a residual pixel-wise semantic segmentation, which segmented the fetal heart and thoracic contours in a 4-chamber view for assessing an enlarged fetal heart condition. The accuracy of intersection-over-union (IoU) and dice coefficient similarity (DCS) is used for model validation to further regulate the evaluation procedure. We use 1174 US images, comprising about 560 enlarged heart images, and about 614 normal heart images. Out of these data, 248 images are used for unseen data, and the remaining for training/validation processes. The performance of the proposed model, when tested on unseen data, achieved satisfactory results with 97.71% accuracy, 90.36% IoU, and 94.93% DCS. These metrics collectively demonstrate the satisfactory performance of the proposed model compared to existing segmentation models. The outcomes underscore that the proposed model establishes a state-of-the-art standard for enlarged fetal heart detection.
BLOOD VESSEL SEGMENTATION IN RETINAL IMAGES USING CONVOLUTIONAL NEURAL NETWORK VV-NET METHOD Sinta Bella Agustina; Erwin, Erwin; Desiani, Anita; Saputra, Tommy
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1723

Abstract

The retina is susceptible to various diseases that can be fatal if not treated quickly. Image processing is currently very helpful for doctors to detect retinal diseases faster so that retinal diseases can be treated immediately. The first step in image processing is to improve the quality of retinal images affected by noise, aiming to increase accuracy in the process of segmentation and image extraction. accurate segmentation of retinal blood vessels is the first step in disease detection. The process of segmentation and analysis of retinal blood vessels has an important role in assisting medical professionals in identifying the severity of a disease. Image quality improvement steps in preprocessing use grayscale, median filter (denoising), and clahe. The method used for blood vessel segmentation is CNN VV-Net. Evaluation of the results of applying image quality enhancement and segmentation techniques using the VV-Net method was performed on the DRIVE, STARE, and CHASEDB_1 datasets at both stages, training and testing. The measurement results of blood vessel segmentation using the CNN VV-net method on the DRIVE dataset (accuracy 96.27%, sensitivity 84.38%, precission 75.95%, and jaccard score 66.28%), STARE dataset (accuracy 96.58%, sensitivity 82.78%, precission 76.73%, and jaccard score 65.38%), and CHASEDB_1 dataset (accuracy 97.04%, sensitivity 83.55%, precission 76.72%, and jaccard score 66.40%). From the three datasets used, the CHASEDB_1 dataset obtained better results than the DRIVE and STARE datasets.
Co-Authors Anita Desiani Armansyah Armansyah Darmawahyuni, Annisa Dian Palupi Rini Erwin Erwin Fachry Abda El Rahman Fauzan, Haidil Febriady, Mukhlis Haslindah, Andi Hernando, Luki Hidayat, Hendri Idawati Idawati M. Ansyar Bora Marchiano, Herli Muhammad Naufal Rachmatullah Muhammad Taufik Roseno, Muhammad Taufik Nurqolbiah, Fatihani Paulina Suri, Ghea Primanita, Anggina Putri Mirani Ririt Dwiputri Permatasari Salsabila, Tia Andini Sanusi Sapitri, Ade Iriani Shilah, Nur Sinta Bella Agustina Siti Nurmaini Sofian, Hocky Syaputra, Hadi Teisnajaya, Usep Twenty Agustine, Grace Ubaidillah Ubaidillah Utari, Aspirani Winda Kurnia Sari Yudha Pratomo Yuritanto, Yuritanto