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All Journal Syntax Jurnal Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Applied Information System and Management Jurnal Informasi dan Komputer JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah Mahasiswa Fakultas Hukum Universitas Malikussaleh
savina, savina
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Other

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IMPLEMENTASI DEEP LEARNING DALAM MENGIDENTIFIKASI KERETAKAN BAN savina, savina; Adam, Riza Ibnu; Rozikin, Chaerur
Jurnal informasi dan komputer Vol 12 No 01 (2024): Jurnal Informasi dan Komputer yang terbit pada tahun 2024 pada bulan 4 (April)
Publisher : LPPM Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia

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Abstract

Tires are a crucial component of vehicles that play an important role. The functions of tires include reducing vibrations from road irregularities, protecting the wheels from wearing out quickly, and providing ease of movement while driving. Due to their vital nature, it is important to maintain the condition of tires to ensure passenger safety and comfort. Excessive tire usage can lead to damage such as cracks. Cracks in tires can occur due to poor weather conditions and road conditions. Cracks in tires refer to a condition where the tire loses its flexibility and traction capabilities while driving. In fact, research data shows that 80% of traffic accidents on highways occur due to indications of tire damage. Prompt handling and regular checks are required to address and optimize tire damage. The methods used to check tire conditions previously were done manually and relied on human labor. These methods are considered ineffective in identifying tire cracks. In this study, a Deep Learning model using the Transfer Learning ShuffleNet approach was developed to automatically classify tire images in identifying tire cracks. The main objective of this research is to determine the best method in identifying tire cracks and measure the performance of the developed model. In the development of this model, testing was conducted using 10 different scenarios on the created model to find the best method for achieving optimal testing accuracy. The best results obtained were an accuracy of 78% using the ADAM optimizer and 75% using the RMSprop optimizer. Therefore, it can be concluded that the Transfer Learning ShuffleNet method is efficient and capable of accurately detecting tire cracks. This research also successfully determined the best parameters such as the number of epochs, dropout layers, and optimizer in model creation to achieve optimal results. Through the adoption of Transfer Learning ShuffleNet, this research contributes to the development of tire damage detection technology aimed at improving safety and driving comfort.
Analisis Sentimen terhadap Kebijakan Food Estate Menggunakan Algoritma Support Vector Machine Mufidah, Ratna; Triana, Heru; Savina, Savina
Syntax : Jurnal Informatika Vol. 13 No. 01 (2024): Mei 2024
Publisher : Universitas Singaperbangsa Karawang

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Abstract

The food estate policy has become a key topic in public discussions in Indonesia regarding food security. However, its implementation has sparked reactions on social media, ranging from positive to negative and neutral. This study aims to analyze public sentiment towards the food estate policy using the Support Vector Machine (SVM) algorithm. SVM was chosen for its proven effectiveness in text classification, and previous studies have demonstrated high accuracy in sentiment analysis. Data were collected from the social media platform X using scraping techniques, followed by data processing. The processed data were then classified into three sentiment categories (positive, negative, and neutral) using SVM with linear, RBF, polynomial, and sigmoid kernels. The eval__uation results show that SVM with a linear kernel and parameter C=2 provided the best performance, achieving 79% accuracy, 80% precision, 79% recall, and an F1-score of 79%. These findings indicate that SVM is capable of accurately classifying public sentiment, offering valuable insights for policymakers in eval__uating the social impact of the policy.
OPTIMALISASI MANAJEMEN PROSES BISNIS YOLAH AUTHENTIC FROZEN YOGURT MELALUI ANALISIS SWOT UNTUK MENINGKATKAN DAYA SAING DI ERA DIGITAL Savina, Savina; Antonius Alijoyo, Franciskus; Shofiah Hilabi, Shofa
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12614

Abstract

Usaha Mikro Kecil dan Menengah (UMKM) di industri makanan sehat, seperti Yolah Authentic Frozen Yogurt menghadapi tantangan serius dalam mempertahankan daya saing di tengah disrupsi digital. Salah satu kendala utama adalah keterbatasan sumber daya, infrastruktur dan rendahnya literasi teknologi yang menghambat optimalisasi pemasaran digital. Akibatnya, manajemen proses bisnis Yolah menjadi kurang efektif dalam mendukung proses bisnis. Penelitian ini bertujuan merumuskan solusi strategis dan rekomendasi praktis untuk meningkatkan manajemen proses bisnis Yolah, dengan menekankan pentingnya pemasaran digital sebagai alat untuk menghadapi disrupsi dan memastikan keberlanjutan usaha di era transformasi teknologi. Metode yang digunakan adalah pendekatan SWOT dengan pengumpulan data deskriptif kualitatif melalui wawancara mendalam. Data primer diperoleh dari wawancara dengan pemangku kepentingan utama, sementara data sekunder dikumpulkan melalui studi literatur terkini. Metode ini dirancang untuk menganalisis faktor internal dan eksternal yang memengaruhi kinerja pemasaran digital Yolah. Hasil penelitian menunjukkan bahwa pemasaran digital Yolah belum dikelola secara optimal. Meskipun Yolah memiliki kualitas produk yang baik, pemanfaatan teknologi digital dalam strategi pemasaran masih terbatas. Penelitian ini merekomendasikan pengembangan strategi pemasaran digital yang lebih terarah serta peningkatan literasi teknologi digital untuk mendongkrak daya saing UMKM di era digital
PERTANGGUNGJAWABAN PIDANA TERHADAP IBU PENGIDAP BABY BLUES SYNDROME YANG MELAKUKAN TINDAK PIDANA PENGANIAYAAN ANAK Savina, Savina; Saputra, Ferdy; Subaidi, Joelman
Jurnal Ilmiah Mahasiswa Fakultas Hukum Universitas Malikussaleh Vol. 8 No. 1 (2025): (Januari)
Publisher : Fakultas Hukum Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jimfh.v8i1.19927

Abstract

Baby blues syndrome adalah suatu ganguan psilogis yang di alami oleh seorang ibu pasca melahirkan. Gangguan ini akan meninbulkan perubahan emosi yang tidak stabil, kecemasan berlebihan dan mudah marah bahkan hilang pengendalian diri. Dalam kondisi gangguan ini ibu dapat melakukan perbuatan pidana, salah satumya adalah tindak pidana penganiayaan terhadap anak. Adapun tujun dari penelitian ini adalah untuk mengetahui bagaimana pertanggungjawaban pidana terhadap ibu pengidap baby blues syndrome yang melakukan tindak pidana penganiyaan anak dan bagaimana kemampuan bertanggungjawab serta bagaimana bentuk perlindungan hukum terhadap anak sebagai korban tindak pidana penganiayaan. Metode penelitian yang digunakan adalah yuridis normatif pendekatan yang dilakukan dalam penelitian ini ialah pendekatan perundang- undangan dan menggunakan data sekunder. Hasil dari penelitian menunjukkan bahwa ibu pengidap baby blues syndrome yang melakukan tindak pidana penganiayaann terhadap anak tidak dapat dimintai pertanggungjawaban pidana, karena baby blues syndrome masuk dalam kategori Orang Dengan Masalah Kejiwaan (ODMK), sebagaimana yang telah dijelaskan dalam pasal 44 Ayat (1) KUHP bahwa orang yang cacat jiwanya tidak dapat mempertanggungjwabakan atas Tindakan nya karna tidak sehat akal nya. Saran dari penelitian ini pertanggungjawaban pidana ibu yang mengalami gangguan kejiwaan yang melakukan tindak pidana penganiayaan terhadap anak nya memiliki pengananganan khusus dalam memberikan penanganan seperti merehabilitasi terdakwa kedalam rumah sakit jiwa sampai keadaanya kembali pulih seperti semula dan tidak berlanjut ke hal yang lebih serius.
Pemanfaatan Data Analitik dalam Big Data: Studi Kasus Implementasi di Pemerintahan Hilabi, Shofa Shofiah; Savina, Savina; Khairunisa, Sabnabila
JATISI Vol 12 No 1 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i1.10916

Abstract

This study examines the use of Big Data in public health management in Indonesia, with a special emphasis on the opportunities, problems, and impacts of using evidence-based data on decision-making. This research was conducted using a descriptive qualitative approach, which involved the analysis of secondary data from a number of relevant journal articles. The results of the study show that big data improves health policy, decision-making processes, and understanding of public health trends. Examples of practices that occurred during the COVID-19 pandemic show how big data analytics helps governments monitor vaccine deployment and distribution in real time. However, major issues such as lack of technological infrastructure, limited human resources, and data privacy issues continue to emerge. Research shows that with strengthened technology infrastructure, employee training, and better data security policies, the implementation of Big Data has great potential to improve the efficiency of public health management. According to this study, investment in information technology must be made, and also a policy framework that supports data integration between government agencies must be created.
Deep Learning Model for Automated Tire Crack Detection Using Convolutional Neural Networks Hilabi, Shofa Shofiah; Fauzi, Ahmad; Savina, Savina
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.46226

Abstract

Tire cracks pose a significant safety risk, as undetected defects can lead to severe accidents. Traditional inspection methods rely on manual visual assessments, which are prone to human error. This study proposes an automated tire crack detection system using Convolutional Neural Networks (CNN), leveraging transfer learning techniques to improve accuracy and generalization. A dataset of 600 tire images was collected and preprocessed, including augmentation techniques such as rotation, flipping, and brightness adjustments. The CNN model was trained with different optimizers, including Adam and Stochastic Gradient Descent (SGD), to compare their performance. Experimental results indicate that Adam achieved the highest test accuracy of 78.3% with the lowest test loss of 53%, while SGD required more epochs to reach optimal performance. This study demonstrates the feasibility of deep learning-based automated tire inspection, providing a scalable alternative to traditional methods. Future research should focus on optimizing model architectures, expanding datasets, and integrating real-time detection for industrial applications.
Co-Authors Antonius Alijoyo, Franciskus Fauzi Ahmad Muda Ferdy Saputra Hilabi, Shofa Shofiah Khairunisa, Sabnabila Mufidah, Ratna Riza Ibnu Adam, Riza Ibnu Rozikin, Chaerur Shofiah Hilabi, Shofa Subaidi, Joelman Triana, Heru