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All Journal Elkom: Jurnal Elektronika dan Komputer International Journal of Computing and Informatics (IJCANDI) Jurnal Pengabdian Pada Masyarakat ILKOM Jurnal Ilmiah Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Informasi dan Teknologi Indonesian Journal of Electrical Engineering and Computer Science G-Tech : Jurnal Teknologi Terapan Jurnal Abdidas Indonesian Journal of Data and Science Jurnal Teknik Informatika (JUTIF) Jurnal Minfo Polgan (JMP) Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat Bulletin of Social Informatics Theory and Application Engineering: Journal of Mechatronics and Education Prosiding SeNTIK STI&K JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Siska Anraeni
Teknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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SOSIALISASI TEKNOLOGI PENGOLAHAN CITRA DIGITAL DALAM PENDETEKSIAN KEMATANGAN BUAH BERBASIS ANDROID BAGI KELOMPOK TANI PADA BALAI PENYULUHAN PERTANIAN KEC.TOMPOBULU, KAB.MAROS Anraeni, Siska; Herman, Herman
JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat Vol 3, No 3 (2023): Sipissangngi Volume 3, Nomor 3, September 2023
Publisher : Lembaga Penelitan dan Pengabdian Masyarakat, Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/sipissangngi.v3i3.3777

Abstract

Permasalahan yang ditemukan pada mitra kelompok Tani Balai Penyuluhan Pertanian (BPP) Kec.Tompobulu, Kab.Maros yaitu dalam melakukan penentuan kematangan buah labu siam masih secara manual dan terkadang terjadi perbedaan persepsi dari masyarakat. Sehingga identifikasi kematangan pada buah labu siam menjadi kurang optimal. Solusi yang diusulkan antara lain memberikan sosialisasi dan pelatihan penggunaan aplikasi yang dapat mengidentifikasi tingkat kematangan buah labu siam melalui smartphone android yang dimiliki oleh masyarakat khususnya kelompok Tani. Kegiatan pengabdian pada masyarakat ini mendapatkan pencapaian antara lain peserta dari kelompok Tani dan Penyuluh sebanyak 15 orang mendapatkan pelatihan aplikasi pendeteksi kematangan buah labu siam berbasis android (ADEMBUL), mampu secara mandiri dan terampil dalam menggunakan aplikasi. Dari hasil kuisioner didapatkan nilai tertinggi sebesar 80% peserta mengatakan mayoritas pengguna dapat belajar secara cepat dan sangat percaya dalam menggunakan aplikasi ADEMBUL.
ANALISIS REKAMAN VIDEO CCTV DENGAN TEKNIK ENHANCEMENT MENGGUNAKAN METODE NATIONAL INSTITUTE OF JUSTICE (NIJ) Erick Irawadi Alwi; Siska Anraeni
Elkom : Jurnal Elektronika dan Komputer Vol 17 No 1 (2024): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v17i1.1563

Abstract

Crime and criminality are increasing by utilizing electronic and digital devices, such as CCTV (closed circuit television) security devices, smartphones, and other electronic devices that have video features, record and store perpetrator data. CCTV recording files are sometimes unclear, so video forensic software is needed to clarify the object so that it can be used as evidence in court. The method used in this research is the National Institute of Justice (NIJ) method and enhancement techniques to clarify the image frame objects of CCTV video recordings using Amped Five forensic image and video tools. The results of the analysis of the evidence concluded that they had succeeded in identifying the vehicle number plate of the alleged perpetrator by carrying out an enhancement process (improving the quality) of the image object. The enhancement process is carried out by utilizing the optical debluring feature of the amped five forensic video software, in settings by increasing the size from 1 to 2 and increasing the noise value from 0.0100 to 0.6310 so it looks clearer than before.
Innovative CNN approach for reliable chicken meat classification in the poultry industry Anraeni, Siska; Mustari, Muhid; Ramdaniah, Ramdaniah; Kurniati, Nia; Mubarak, Syahrul
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.686

Abstract

In response to the burgeoning need for advanced object recognition and classification, this research embarks on a journey harnessing the formidable capabilities of Convolutional Neural Networks (CNNs). The central aim of this study revolves around the precise identification and categorization of objects, with a specific focus on the critical task of distinguishing between fresh and spoiled chicken meat. This study's overarching objective is to craft a robust CNN-based classification model that excels in discriminating between objects. In the context of our research, we set out to create a model adept at distinguishing between fresh and rotten chicken meat. This endeavor holds immense potential in augmenting food safety and elevating quality control standards within the poultry industry. Our research methodology entails meticulous data collection, which includes acquiring high-resolution images of chicken meat. This meticulously curated dataset serves as the bedrock for both training and testing our CNN model. To optimize the model, we employ the 'adam' optimizer, while critical performance metrics, such as accuracy, precision, recall, and the F1-score, are methodically computed to evaluate the model's effectiveness. Our experimental findings unveil the remarkable success of our CNN model, with consistent accuracy, precision, and recall metrics all reaching an impressive pinnacle of 94%. These metrics underscore the model's excellence in the realm of object classification, with a particular emphasis on its proficiency in distinguishing between fresh and rotten chicken meat. In summation, our research concludes that the CNN model has exhibited exceptional prowess in the domains of object recognition and classification. The model's high accuracy signifies its precision in furnishing accurate predictions, while its elevated precision and recall values accentuate its effectiveness in differentiating between object classes. Consequently, the CNN model stands as a robust foundation for future strides in object classification technology. As we peer into the horizon of future research, myriad opportunities beckon. Our CNN model's applicability extends beyond chicken meat classification, inviting exploration across diverse domains. Furthermore, the model's refinement and adaptation for specific challenges represent an exciting avenue for future work, promising heightened performance across a broader spectrum of object recognition tasks.
Peningkatan Kualitas Citra Iris Mata Menggunakan Operasi Piksel Dan Ekualisasi Histogram Untuk Pengklasifikasian Kondisi Kesehatan Ginjal Siska Anraeni; Herman
Prosiding Seminar SeNTIK Vol. 2 No. 1 (2018): Prosiding SeNTIK 2018
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pengolahan citra pada pemrosesan awal (pre-processing) penelitian sebelumnya belum dilakukan secara lebih dalam dan mendetail. Sehingga penelitian ini bertujuan untuk melakukan perbaikan kualitas citra iris mata menggunakan operasi piksel dan ekualisasi histogram terhadap proses pengklasifikasian ginjal ke dalam kondisi normal atau tidak normal. Metode operasi piksel yang digunakan yaitu peningkatan kecerahan (brightness), perenggangan kontras (contrast), kombinasi kecerahan dan kontras serta ekualisasi histogram. Hasil dari penelitian ini yaitu aplikasi dapat: 1) Memperbaiki kualitas citra menggunakan peningkatan kecerahan sebesar 50 piksel, perenggangan kontras sebesar 2,5 kali piksel, kombinasi kecerahan dan kontras sebesar 50 piksel dan 1,5 kali piksel dan ekualisasi histogram; 2) Melakukan pengklasifikasian citra iris mata yang menunjukkan ginjal normal dan tidak normal berdasarkan hasil perbaikan kualitas citra terhadap 10 citra latih dan 10 citra uji dengan tingkat akurasi sebesar 70%
Receipt Scanning with EasyOCR and ChatGPT-4o in a Mobile Finance App: an Agile Kanban Approach Septiawan, M. Fiqry; Anraeni, Siska; Ramdaniah, Ramdaniah
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.7822

Abstract

Technological advancements have provided convenience for Generation Z in managing finances; however, many are still not accustomed to recording their financial activities regularly. Shopping receipts, which should serve as proof of transactions, are often ignored or poorly managed, despite their important role in tracking expenses. Therefore, this research aims to develop an Android-based financial recording application capable of handling both manual input and automated recording through receipt scanning using Optical Character Recognition (OCR) technology. The findings indicate that ChatGPT-4o significantly outperforms EasyOCR by providing more consistent accuracy and faster, stable processing, making it a more reliable solution for receipt-based financial recording. Developed using the Agile Kanban method, the application was validated through alpha testing and proven to function properly across all features. Beyond practical benefits for users, this research also contributes to the financial technology literature by demonstrating the integration of large language models (LLM) to enhance OCR performance in mobile finance applications.
Application Of The Least Square Method For Website Based Shrimp Sales Prediction Muhsina, Muhsina; Siska Anraeni; Muhammad Arfah Asis
Engineering: Journal of Mechatronics and Education Vol. 1 No. 1 (2024): Engineering: Journal of Mechatronics and Education
Publisher : Yayasan Insan Mulia Bima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59923/mechatronics.v1i1.18

Abstract

UD Arif is a company engaged in the business of buying and selling shrimp, this company is located on Jalan Soreang No.127 Pitue, Desa Pitue, Kec. Ma'rang, Kab.Pangkep. The problem that is often experienced at UD Arif is the amount of shrimp availability that does not match customer demand because shrimp quickly deteriorate and sales fluctuate due to market demand, such as the size of the shrimp that must be stocked, the quality of the shrimp, and too much stock on the market resulting in Shrimp sales decline Therefore the aim of this study is to produce an application that can make UD Arif's youth easier in predicting shrimp sales. So the method used in this study is the least squares method, which is a method that can handle data that experiences ups and downs, where it is influenced by seasons and trends. The result of this research is to produce a website that can predict shrimp sales. These results are known by means of correlation, namely looking for a relationship between the original data and the predicted data calculated using Excel. From this correlation method, it is known that with 12 months of test data, it is possible to have a vannamei shrimp test with a MAPE result of 3.7%.
ANALISIS REKAMAN VIDEO CCTV DENGAN TEKNIK ENHANCEMENT MENGGUNAKAN METODE NATIONAL INSTITUTE OF JUSTICE (NIJ) Erick Irawadi Alwi; Siska Anraeni
Elkom: Jurnal Elektronika dan Komputer Vol. 17 No. 1 (2024): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v17i1.1563

Abstract

Crime and criminality are increasing by utilizing electronic and digital devices, such as CCTV (closed circuit television) security devices, smartphones, and other electronic devices that have video features, record and store perpetrator data. CCTV recording files are sometimes unclear, so video forensic software is needed to clarify the object so that it can be used as evidence in court. The method used in this research is the National Institute of Justice (NIJ) method and enhancement techniques to clarify the image frame objects of CCTV video recordings using Amped Five forensic image and video tools. The results of the analysis of the evidence concluded that they had succeeded in identifying the vehicle number plate of the alleged perpetrator by carrying out an enhancement process (improving the quality) of the image object. The enhancement process is carried out by utilizing the optical debluring feature of the amped five forensic video software, in settings by increasing the size from 1 to 2 and increasing the noise value from 0.0100 to 0.6310 so it looks clearer than before.
Perbandingan Kinerja Word Embedding dalam Analisis Sentimen Ulasan Pengguna Aplikasi Perjalanan Pahendra, Muhammad Agung Maugi; Anraeni, Siska; Ilmawan, Lutfi Budi
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 1 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i1.9681

Abstract

Traveloka, as one of the leading travel booking platforms, has achieved more than 50 million downloads on Google Play Store. This achievement shows the high interest and trust of users in the services offered. However, user reviews indicate that there are some issues with the app's performance and stability that need to be taken into account. This research compares the performance of the Word2Vec and ELMo word embedding methods using the BiLSTM model in sentiment analysis of Traveloka application reviews. The research results show that the BiLSTM model with Word2Vec has an accuracy of 76.13%, precision 75.22%, and F1-measure 76.58%, better than the model with ELMo which has an accuracy of 74.38%, precision 70.49%, and F1-measure 74.40%. The BiLSTM model with Word2Vec is more effective in sentiment analysis of Traveloka reviews, helping identify and address user issues to improve service quality and user satisfaction.
Co-Authors Ainul Yaqin Anjasani Aisyah Aisyah Alfian Putra Ramadhan Andi Alfian Pratama Putra Andi Nurul Dzulhijjah Darwis, Herdianti Dewi, Nabila Vita Erick Irawadi Alwi Erick Irawadi Alwi, Erick Irawadi Erika Riski Melani Fadhylah Nur Rezkyqah Fitriani Hasbullah Fitriyani Umar Furqaan Ismail Gaffar, Andi Widya Mufila Halim, Andi Ainun Dzariah Harlinda Lahuddin Hasnita Hasnita Herdiansya Herdiansya Herdianti Darwis Herdianti Darwis Herdianti Herdianti Herman Herman Herman Hilma Aszahrah Ihwana As’ad Imada, Anugerah Indrabayu Indrabayu Ingrid Nurtanio Iqbal, Iwi Kurnia Irawati Irawati Januaril Aditya Samudra La Ode Abdurrahman Wahid Pattawari Lokapitasari Belluano, Poetri Lestari Lutfi Budi Ilmawan, Lutfi Budi Manga, Abdul Rachman Mardiyyah Hasnawi Mubarak, Syahrul Muh Dasriyanto Saleh Muh. Aliyazid Mude Muhammad Arfah Asis Muhammad Fadhiel Muhsina Muhsina, Muhsina Muliyadi B Mursyid Mursyid Mustari, Muhid Nia Kurniati Nia Kurniati Nugraha Wanaspati Nur Amanah Nur Hikmah Amir Nursafi'at Nursafi'at Pahendra, Muhammad Agung Maugi Pomalingo, Suwito Ramdan Satra Ramdaniah Ramdaniah Rifqatul Mukarramah Rina Junita Basri Septiawan, M. Fiqry St. Hajrah Mansyur Sugiarti Sugiarti Sugiarti, Sugiarti Syafie, Lukman Syahrul Mubarak Abdullah Takdir Zulhaq Dessiaming Tasrif Hasanuddin Veithzal Rivai Zainal Wanaspati, Nugraha Yudha Nugraha Syailendra Yusrina Mukhlis