Article Per Year (5 Year)
p-Index From 2020 - 2025
1.168
P-Index
This Author published in this journals
All Journal Teika Jurnal Ilmu Komputer dan Agri-Informatika Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Mandiri IT Jurnal Medika: Medika
Marjusalinah, Anna Dwi
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Perancangan Sistem Informasi Monitoring Dosen Pembimbing Mahasiswa Kerja Praktek (KP) Willy, Willy; Firnando, Ricy; Gumay, Naretha Kawadha Pasemah; Marjusalinah, Anna Dwi; Ariani, Ardina; Febriady, Mukhlis
Generic Vol 16 No 1 (2024): Vol 16, No 1 (2024)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/generic.v16i1.179

Abstract

Ilmu Pengetahuan dan Teknologi saat ini begitu pesat dalam perkembangannya, tidak terkecuali dalam bidang dunia digital, dalam hal ini ketua jurusan dan Koordinator program studi bahkan wakil dekan bidang akademik sangat kesulitan untuk memonitoring mahasiswa yang melakukan bimbingan akademik dan konsultasi kerja praktek. Bahkan sangat banyak kasus tidak mengetahui perkembangan dan keaktifan mahasiswa terhadap dosen pembimbing dan juga kurangnya informasi berapa sering mahasiswa tersebut melakukan mimbingan terhadap dosen pembimbing akademik sampai mahasiswa tersebut melakukan kerja praktek, sehingga dibutuhkan sebuah sistem informasi untuk memonitoring antara dosen pembimbing akademik terhadap mahasiswa dengan menggunakan metode agile, sehingga informasi tersebut dapat menjadi acuan oleh para pimpinan. Hasil penelitian akan menjadi acuan untuk membangun sistem informasi yang diharapkan dapat membantu proses monitoring antara dosen pembimbing dan mahasiswa.
Pengukuran Kesiapan Adopsi Teknologi Informasi Pada Poktan Hidup Baru Dengan Metode Digital Literacy Index Az Zahra, Cindy Putri; Oktadini, Nabila Rizky; Sevtiyuni, Putri Eka; Marjusalinah, Anna Dwi; Akbaroka, Leo
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 11 No. 2 (2024)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.11.2.143-155

Abstract

Although advances in digital technology offer great opportunities to improve efficiency and productivity in agricultural sector, its application has been uneven. Adoption of information technology still faces challenges, especially among farmers. This study aims to measure the readiness of information technology adoption in the Kelompok Tani (Poktan) Hidup Baru, Cempaka District, OKU Timur, using the Digital Literacy Index (DLI) which includes four main pillars such as Digital Skill, Digital Ethics, Digital Safety, and Digital Culture. The results show that farmers' digital literacy index has an average of 2.91, lower than the national average of 3.54. The biggest gap is in the digital skills pillar (2.20), signaling difficulties in operating devices. Meanwhile, digital ethics awareness is quite good (2.87). Education level (r = 0.79) and frequency of internet use (r = 0.97) have positive correlations with digital skills. However, the correlation between digital skills and digital safety was weak (r = 0.14), indicating a lack of understanding of cybersecurity. These findings emphasize the need to improve digital literacy to support technology adoption.
Optimization of Tsukamoto FIS Using Genetic Algorithm for Rainfall Prediction in Banyuasin Regency Akbar, Muhammad Rafi; Miraswan, Kanda Januar; Rodiah, Desty; Buchari, Muhammad Ali; Marjusalinah, Anna Dwi
Sriwijaya Journal of Informatics and Applications Vol 5, No 2 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i2.118

Abstract

Indonesia, as a tropical country with high rainfall, heavily relies on accurate rainfall predictions for various critical purposes, including water resource management and extreme weather impact mitigation. One commonly used method is the Tsukamoto Fuzzy Inference System (FIS). However, implementing the Tsukamoto FIS often leads to high error rates. This is attributed to the difficulty in determining the boundaries of fuzzy variable membership functions. To address this issue, this research proposes an innovative approach by optimizing the boundaries of fuzzy membership functions using Genetic Algorithms (GA). The study resulted in a 49.02% reduction in the error rate, decreasing from 76.82% to 27.8%. This method significantly enhances rainfall prediction accuracy and contributes to the advancement of more sophisticated prediction methods. The optimization method proposed in this study also holds potential for application across various atmospheric science contexts.
Perancangan Webiste EXP.CAN dalam Pencarian Resto di Palembang Firnando, Ricy; Willy, Willy; Kawadha Pasemah Gumay, Naretha; Marjusalinah, Anna Dwi; Ariani, Ardina
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.v16i2.191

Abstract

Dalam era digital yang semakin berkembang, kebutuhan akan akses informasi yang cepat dan tepat menjadi semakin penting, terutama dalam menjelajahi ranah kuliner. Palembang, sebagai salah satu kota dengan kekayaan kuliner yang melimpah, membutuhkan platform yang tidak hanya memudahkan para penggunanya dalam menemukan tempat makan berkualitas, tetapi juga memperkaya pengalaman kuliner mereka. Itulah mengapa Explore CAN (EXP.CAN) hadir sebagai solusi yang memadukan kepraktisan dan keberagaman dalam satu platform. Dengan menggabungkan teknologi dan kecanggihan pencarian, EXP.CAN memungkinkan pengguna untuk menemukan tempat makan terbaik di Palembang dengan mudah dan cepat. Fitur-fitur seperti filter untuk mencari tempat makan berdasarkan berdasarkan abjad dan lokasi memungkinkan pengguna untuk menyaring pilihan mereka sesuai dengan preferensi dan keinginan. Tak hanya itu, kemampuan untuk membaca ulasan dari pengguna lain juga memberikan wawasan yang berharga dalam memilih tempat makan yang tepat sesuai dengan selera dan kebutuhan.
Pengukuran Kesiapan Adopsi Teknologi Informasi (IT Adoption) Pada Gapoktan Hidup Baru Kec. Cempaka OKU Timur Dengan Metode Community Readiness Model Akbaroka, Leo; Oktadini, Nabila Rizky; Marjusalinah, Anna Dwi; Sevtiyuni, Putri Eka; Az Zahra, Cindy Putri
TeIKa Vol 14 No 2 (2024): TeIKa: Oktober 2024
Publisher : Fakultas Teknologi Informasi - Universitas Advent Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36342/teika.v14i2.3729

Abstract

This research aims to measure the readiness to adopt information technology in the Gapoktan Life Baru community in East Cempaka OKU District using the Community Readiness Model (CRM) method. CRM measures several aspects, namely Community Effort, Knowledge Towards Issues, Leadership, Community Conditions, Community Knowledge, and Related Resources. This research involved 23 respondents who were members of the New Life Gapoktan. The research results show that the Community Effort (CE) aspect obtained a score of 3.86, Knowledge Towards Issue (KT) 3.32, Leadership (LS) 4.55, Community Conditions (KM) 3.89, Community Knowledge (PM) 3, 45, and Related Resources (ST) 4.06. Based on the overall score, the level of community readiness reached a final score of 3.86, which indicates that the community is at the initiation stage, which means efforts to adopt information technology have begun, but still require further strengthening. These results indicate that society has begun to realize the importance of implementing information technology, although more intensive efforts are still needed to achieve a higher level of readiness in adopting information technology optimally.
Pendampingan Inovasi Kecerdasan Buatan dalam Pengembangan Asesmen Pembelajaran untuk Mendukung Literasi Digital bagi Guru SMP Buchari, Muhammad Ali; Sukemi, Sukemi; Oktadini, Nabila Rizky; Marjusalinah, Anna Dwi; Simarmata, Ruth Helen; Afif, Hasnan
Jurnal Medika: Medika Vol. 4 No. 3 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/7xep0n45

Abstract

The Merdeka curriculum formulates learning that refers to the abilities needed in the era of industrial revolution 4.0 and society 5.0. The assessment is carried out as an effort to measure the level of achievement of learning indicators and collect information on student learning progress in various aspects.  Facts in the field show problems in implementing independent curriculum cognitive assessments in learning. This indicates the high need for preparing independent curriculum assessments, namely diagnostic assessments, formative assessments and summative assessments. Artificial intelligence or Artificial Intelligence (AI) is part of the industrial revolution 4.0 and society 5.0 so that integrating society and technology cannot be avoided.  Artificial Intelligence in mathematics learning has great potential to support learning effectiveness and efficiency. This service is a lecture activity that integrates artificial intelligence evaluation courses into service activities that are integrated with community service. This initiative was implemented to support digital literacy and increase teacher competency in utilizing modern technology, in accordance with the principles of the Independent Curriculum. This activity includes training, mentoring, and evaluation of the use of AI in learning assessment. As a result of this activity, teachers are able to understand the basic concepts of AI, innovation in assessment development, and its implementation in the classroom. Through various face-to-face and online meetings, this activity succeeded in providing new insights for teachers regarding the use of AI technology in learning, although there were challenges related to initial understanding and availability of supporting facilities. With intensive assistance, teachers are able to prepare assessments that are more adaptive and appropriate to student needs, so they are expected to be able to support improving the quality of education in the digital era.
Performance analysis of MobileNetV2 based automatic waste classification using transfer learning Firnando, Ricy; Buchari, Muhammad Ali; Marjusalinah, Anna Dwi; Willy; Abdurahman; Isnanto, Rahmat Fadli
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.451

Abstract

The significant increase in global waste requires innovative and accessible solutions, which aligns with Sustainable Development Goal (SDG) 12, which focuses on reducing the environmental impact of human activities. Automatic waste sorting using Computer Vision and Deep Learning offers a promising alternative to labor-intensive and risky manual methods. This study presents the design, implementation, and comprehensive performance analysis of an automated waste classification system, with a specific focus on evaluating its feasibility on hardware without specialized GPU accelerators. By leveraging transfer learning on a lightweight Convolutional Neural Network (CNN) architecture, MobileNetV2, a model was trained to classify six common waste categories: cardboard, glass, metal, paper, plastic, and other waste. The public “Garbage Classification” dataset from Kaggle, consisting of 2,527 images, was used as the basis for training and validation. The experiment was conducted using the tensorflow-cpu library, which does not require a dedicated GPU accelerator. After 10 training epochs, the model achieved a significant validation accuracy of 86.73%. Computational performance analysis showed an efficient average training time of 31.17 seconds per epoch and a fast average inference time of 14.47 milliseconds per image (~69 FPS) on the validation dataset. These findings demonstrate the feasibility of developing an effective AI-based waste classification system on hardware without a GPU accelerator, providing a realistic performance benchmark for the development of low-cost smart bins with embedded waste sorting solutions in the future, thereby contributing to sustainable waste management practices.
Co-Authors Abdurahman Afif, Hasnan Akbar, Muhammad Rafi Akbaroka, Leo Ariani, Ardina Az Zahra, Cindy Putri Buchari, Muhammad Ali Desty Rodiah Febriady, Mukhlis Gumay, Naretha Kawadha Pasemah Kanda Januar Miraswan Kawadha Pasemah Gumay, Naretha Oktadini, Nabila Rizky Rahmat Fadli Isnanto Ricy Firnando Sevtiyuni, Putri Eka Simarmata, Ruth Helen Sukemi Sukemi Willy Willy, Willy