UIN (Universitas Islam Negeri) Sunan Kalijaga, Yogyakarta: E-Journal Fakultas Sains dan Teknologi
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KAJIAN KOMPARATIF PERFORMANSI HIDROKSIPROPIL METILSELULOSA (HPMC) DAN KARAGENAN SEBAGAI MATRIKS PENGGANTI GELATIN PADA FORMULASI KAPSUL HALAL
No full textGelatin merupakan ekstrak dari protein hewani seperti sapi dan babi yang banyak digunakan dalam industri farmasi. Kapsul pada industri farmasi kebanyakan terbuat dari gelatin yang ada di pasaran seperti babi sehingga kehalalannya tidak terjamin. Polimer Hidroksipropil Metilselulosa (HPMC) dan ekstrak karagenan dapat digunakan sebagai bahan pengganti gelatin untuk pembuatan kapsul. Kedua bahan tersebut terjamin kehalalannya karena terbuat dari tanaman yang diekstrak secara kimia. Kajian ini dilakukan untuk membandingkan potensi dan karakteristik dari HPMC dan karagenan berdasarkan parameter fisikokimia sebagai substituen gelatin di Industri Farmasi Halal. Penelitian ini menggunakan metode studi literatur dengan mengkaji perbedaan karakteristik HMPC dan Karagenan. Hasil penelitian menunjukkan HPMC memiliki nilai laju disintegrasi yang lebih cepat daripada karagenan yaitu 16 menit dan 10 menit tanpa agen pembentuk gel. Pada parameter kekuatan mekanik, karagenan memiliki daya tarik paling kuat di antara gelatin dan HPMC dengan nilai 39,41 Mpa. Kadar air pada cangkang kapsul dari HPMC sebesar 4-6%, sedangkan pada karagenan sebesar 13-17%. Secara morfologi, pori-pori karagenan dapat diamati pada permukaannya pada skala 200 nm, sedangkan HPMC permukaannya halus tanpa pori-pori yang diamati pada skala 30 μm. Waktu hancur yang diperlukan pada polimer HPMC lebih cepat yaitu 16 ± 5 menit dibanding kapsul karagenan yang memerlukan waktu 12-25 menit tergantung pada kombinasi bahan. Namun, spesifikasi kerja karagenan dapat ditingkatkan dengan modifikasi maupun degradasi menjadi monomer. Berdasarkan hasil kajian, HMPC dan karagenan memiliki keunggulan masing-masing sebagai substituen gelatin. HMPC unggul pada laju disintegrasi dan stabilitasnya dalam mempertahankan fleksibilitas pada kelembaban rendah, sedangkan karagenan unggul dalam kekuatan mekanik.  
Price Forecasting of Chili Variant Commodities Using Radial Basis Function Neural Network
No full textThis study addresses the challenge of price instability in chili markets, which can lead to economic losses and inflation. To mitigate this issue, we propose a machine learning model using Radial Basis Function Neural Networks (RBFNN) to predict prices of various chili variants. Our quantitative approach involves a comprehensive data preparation process, including preprocessing and normalization of time series data collected from 2018 to 2022. The RBFNN model is constructed with K-Means clustering for optimal hidden layer configurations and evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results demonstrate promising accuracy, with MAPE error rates below 20% and relatively low RMSE values for large red chili (10.37%, 4484) and curly red chili (14.77%, 5590). Our findings indicate the potential for creating a reliable forecast model for predicting chili prices over 7 days, enabling better supply and demand management. The study\u27s results also suggest that increased training data enhances forecasting accuracy. This research contributes to the development of effective price forecasting models, providing valuable insights for policymakers and stakeholders in the chili industry
Keanekaragaman Jenis Tumbuhan Berkhasiat Obat sebagai Agen Antihipertensi di Kampung Udapi, Papua Barat
No full textPengetahuan lokal mengenai tumbuhan obat sangat penting untuk mendukung pelestarian keanekaragaman hayati dan pengembangan obat tradisional. Penelitian ini bertujuan untuk mendokumentasikan tumbuhan yang digunakan oleh Masyarakat Kampung Udapi Hilir dalam pengobatan hipertensi, serta menganalisis tingkat kepentingan dan spesifisitas penggunaannya. Pengambilan data dilakukan pada bulan Agustus – November 2024, melalui wawancara mendalam terhadap 65 informan yang dipilih secara snowball . Data tumbuhan dikonfirmasi melalui identifikasi taksonomi dan dokumentasi. Analisis kuantitatif menggunakan indeks Relative Frequency of Citation (RFC) dan Fidelity Level (FL) untuk menilai popularitas dan spesifisitas setiap tumbuhan dalam pengobatan hipertensi. Sebanyak 22 spesies dari 16 famili berhasil didokumentasikan. Phaleria macrocarpa menunjukkan nilai RFC tertinggi (1,00) dan FL (100 %), diikuti Pandanus conoideus (RFC 0,98; FL 98 %) serta Zingiber officinale Rosc. (RFC 0,95; FL 95 %).Temuan ini menunjukkan tingginya kepercayaan masyarakat terhadap tumbuhan lokal sebagai terapi hipertensi, dan mendukung perlunya pelestarian pengetahuan tradisional. Penelitian ini memberikan dasar ilmiah bagi eksplorasi lebih lanjut dan pengembangan fitofarmaka berdasarkan sumber daya lokal
Predicting Olympic Medal Trends for Southeast Asian Countries Using the Facebook Prophet Model
No full textThe Olympics are a world-class sporting event held every four years, serving as a meeting place for all athletes worldwide. The Olympics are held alternately in different countries. The Olympics were first held in Athens in 1896 and have now reached the 33rd Olympics, which will be held in Paris in 2024. Significant work has been conducted to develop prediction models, with a primary focus on enhancing the accuracy of predicting Olympic outcomes. However, low-performance regression algorithms are the main problem with prediction. By integrating custom seasonality with the Facebook Prophet prediction model, this study aims to enhance the accuracy of Olympic predictions. The proposed new model involves several steps, including preparing the data and initializing and fitting the Facebook-Prophet model with several parameters such as seasonal mode, annual seasonality, and prior scale. The model is tested using the Olympic dataset (1994–2024). The evaluation results indicate that this prediction model provides a reliable estimate of the total medals earned. On the Olympic Games (1994-2024) dataset, the model exhibits a very low error, as indicated by its MAE, MSE, and RMSE, and achieves an R² score of 0.99, which is close to perfect. This research shows that the model is effective in improving prediction accuracy
Evaluasi Keamanan OTP Firebase pada Aplikasi Android: Perbandingan SAST dan IAST dalam Identifikasi Kerentanan
No full textApplication security is crucial for protecting user data from cyber threats, particularly in Android applications that utilize One-Time Password (OTP)-based authentication. This study evaluates the security of Firebase OTP via email using a combination of Static Application Security Testing (SAST) with Mobile Security Framework (MobSF) and Interactive Application Security Testing (IAST) with AppSweep. The results show that the combination of SAST and IAST is superior to single testing methods due to its wider detection coverage. SAST detects vulnerabilities in static code, while IAST identifies exploits in runtime. The testing showed significant improvements, with high-severity vulnerabilities decreasing from 3 cases in OTP-1 to zero in OTP-5, and the security score increasing from 43 (B) to 78 (A) in MobSF. Meanwhile, the number of vulnerabilities in AppSweep decreased from 14 to 9, with all high-severity vulnerabilities resolved. However, this study still has limitations, such as limited dataset coverage and potential bias from the testing tool. For further improvement, additional research can integrate artificial intelligence to automate vulnerability detection, as well as explore biometric-based authentication to enhance system security even further
Pengujian dan Mitigasi Kerentanan Website Sistem Informasi Akademik Universitas Ma\u27arif Nahdlatul Ulama Kebumen dengan OWASP ZAP: Testing and Mitigation of Website Vulnerabilities in the Academic Information System of Universitas Ma\u27arif Nahdlatul Ulama Kebumen using OWASP ZAP
No full textPenggunaan sistem informasi akademik berbasis web di lingkungan pendidikan tinggi semakin krusial untuk mendukung proses manajemen data akademik. Namun, tingginya ketergantungan pada aplikasi web juga meningkatkan risiko terhadap serangan siber. Website Sistem Informasi Akademik Universitas Ma’arif Nahdlatul Ulama Kebumen sempat mengalami insiden peretasan yang menyebabkan tampilan berubah menjadi iklan judi online, meskipun saat ini telah dipulihkan. Berdasarkan insiden tersebut, tujuan penelitian ini dilakukan untuk mengidentifikasi potensi kerentanan lainnya dan memberikan rekomendasi mitigasi. Penelitian menggunakan metode pengujian keamanan berbasis OWASP Web Security Testing Guide (WSTG) dan alat bantu OWASP Zed Attack Proxy (ZAP). Hasil pengujian menunjukkan adanya tiga kerentanan utama, yaitu Content Security Policy (CSP) Header Not Set, HTTP to HTTPS Insecure Transition in Form Post, dan Missing Anti-clickjacking Header. Kendati tidak ditemukan celah XSS aktif dan semua transmisi data telah dienkripsi melalui HTTPS, sistem tetap belum memiliki perlindungan terhadap Clickjacking. Mitigasi yang direkomendasikan mencakup penerapan header CSP, konfigurasi HSTS, serta penambahan X-Frame-Options atau frame-ancestors. Implementasi mitigasi ini diharapkan dapat meningkatkan keamanan sistem informasi akademik dari potensi serangan siber di masa mendatang.
Kata kunci: Keamanan Website, OWASP ZAP, Wireshark, XSS, Clickjacking, OWASP Top 10
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The use of web-based academic information systems in higher education has become increasingly vital for managing academic data. However, this reliance on web applications also increases the risk of cyberattacks. The Academic Information System website of Universitas Ma’arif Nahdlatul Ulama Kebumen previously experienced a hacking incident in which the display was altered to show online gambling advertisements, although it has since been restored. This research aims to identify other potential vulnerabilities and provide mitigation recommendations. The study employs security testing based on the OWASP Web Security Testing Guide (WSTG) and utilizes the OWASP Zed Attack Proxy (ZAP) tool. The results reveal three main vulnerabilities: Content Security Policy (CSP) Header Not Set, HTTP to HTTPS Insecure Transition in Form Post, and Missing Anti-clickjacking Header. Although no active XSS exploit was found and all data transmissions were encrypted via HTTPS, the system lacks protection against clickjacking attacks. Recommended mitigation includes implementing CSP headers, enabling HTTP Strict Transport Security (HSTS), and adding X-Frame-Options or frame-ancestors directives. These measures are expected to enhance the security of the academic information system and protect user data from future cyber threats.
Keywords: Website Security, OWASP ZAP, Wireshark, XSS, Clickjacking, OWASP Top 1
Pengujian Kerentanan Website Menggunakan Metode Penetration Testing Dengan OWASP (Studi Kasus : Pemerintah Kabupaten Semarang)
No full textKeamanan website merupakan aspek yang sangat penting dalam menjaga integritas, ketersediaan, dan kerahasiaan data, terutama bagi instansi pemerintahan yang mengelola informasi publik dan sensitif. Serangan siber yang semakin kompleks dan beragam dapat menyebabkan kebocoran data, peretasan sistem, hingga gangguan layanan yang berdampak pada kepercayaan publik. Oleh karena itu, diperlukan pengujian keamanan secara menyeluruh untuk mengidentifikasi dan mengatasi potensi kerentanan sebelum dapat dimanfaatkan oleh pihak yang tidak bertanggung jawab. Penelitian ini bertujuan untuk menguji keamanan dan kerentanan website Pemerintah Kabupaten Semarang guna meningkatkan standar keamanannya dengan menggunakan metode penetration testing berbasis standar OWASP (Open Web Application Security Project). Metode ini mencakup beberapa tahapan utama, yaitu perencanaan dan pengumpulan informasi, analisis kerentanan, eksploitasi celah keamanan, serta pelaporan hasil beserta rekomendasi mitigasi yang diperlukan. Pengujian dilakukan dengan mensimulasikan berbagai jenis serangan siber untuk mengidentifikasi celah keamanan yang dapat dieksploitasi oleh peretas. Implementasi dari rekomendasi yang diberikan agar dapat digunakan dalam meningkatkan ketahanan website Pemerintah Kabupaten Semarang terhadap ancaman siber, sehingga dapat mencegah kebocoran data, memastikan layanan tetap berjalan dengan baik, serta meningkatkan kepercayaan publik terhadap keamanan sistem pemerintahan berbasis digital.
Kata kunci: Cybersecurity, Penetration Testing, OWASP, Keamanan Website, Pemerintah Kabupaten Semarang
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Website Vulnerability Testing Using the Penetration Testing Method with OWASP (Case Study: Semarang Regency Government)
Website security is an important aspect in maintaining the integrity, availability, and confidentiality of data, particularly for government institutions that manage public information and sensitive records. With the increasing complexity of cyberattacks, the risks of data breaches, system intrusions, and service disruptions may significantly undermine public trust in digital government services. This study aims to assess the security level of the official website of the Semarang Regency Government by applying the penetration testing method based on the OWASP Top 10 standard of 2021, which involves several stages, including planning and information gathering, vulnerability analysis, exploitation, and reporting of findings with corresponding mitigation recommendations. The testing process was conducted by simulating various cyberattacks categorized under OWASP Top 10 to identify exploitable vulnerabilities. The results quantitatively revealed the presence of high-risk vulnerabilities, namely SQL Injection and Cross-Site Scripting (XSS), identified across several subdomains of the government website. These vulnerabilities may allow attackers to steal, manipulate, or misuse critical data, as well as disrupt the continuity of public services. Based on these findings, this research provides technical recommendations such as strengthening input validation, enhancing application security configuration, and implementing continuous monitoring. The application of these mitigation steps is expected to improve the resilience of the Semarang Regency Government website against cyber threats, prevent data breaches, ensure service availability, and reinforce public trust in digital government systems.
Keywords: Cybersecurity, Penetration Testing, OWASP, Website Security, Semarang Regency Governmen
Performance Evaluation of Long Short-Term Memory for Chili Price Prediction
No full textGrocery prices often experience fluctuations in several regions of Indonesia, such as East Java Province. One of the commodities affected is chili, including both red chilies and bird’s eye chilies. Predictive steps that utilise machine learning, such as Long Short-Term Memory (LSTM), can be taken to estimate the next price of chili, with the expectation that the authorities can implement the appropriate strategy. LSTM is a network that was developed from RNN networks in previous times by offering a longer cell memory, allowing for the storage of more information. This research focuses on determining whether the LSTM network can be applied to the task of chili price prediction and identifying the suitable architecture and hyperparameter configuration for this case. For this reason, the experimental method is employed by testing several predetermined variables to determine the optimal architecture and hyperparameter configuration. The results of this research demonstrate that the LSTM network can be effectively applied in this case, and the obtained architecture and optimal hyperparameter configuration are consistent for both types of chilies, namely red chilies and bird’s eye chilies. For red chili, the best RMSE value that can be produced is 1751.890 and 1888.741 for bird’s eye chili
Analisis Sentimen Ulasan Pengguna Aplikasi Alfagift Menggunakan Random Forest
No full textAlfagift is a mobile application developed by Alfamart to support online ordering, featuring promotions, transactions, ordering, and delivery from the nearest point based on the consumer’s address. User feedback on the Google Play Store reveals mixed sentiments, including both positive and negative responses, which developers can use as material to improve the application’s quality. This study focuses on assessing the sentiment of Alfagift app user reviews using the Random Forest algorithm. A total of 4,379 review data points were collected from the Google Play Store and grouped into two categories: positive and negative sentiment. The research steps include data collection, data labeling, data preprocessing, word weighting, dividing the data into training and testing sets, implementing the Random Forest algorithm, and model evaluation. The test results show that the Random Forest algorithm achieves an accuracy of 97.6% and an AUC of 0.98, which falls into the category of excellent classification. This research is expected to contribute to application developers’ understanding of user perceptions, enabling them to improve application quality and increase overall user convenience
Deteksi Diabetes Mellitus dengan Menggunakan Teknik Ensemble XGBoost dan LightGBM
No full textDiabetes mellitus is a metabolic disease characterized by elevated blood sugar levels due to impaired insulin secretion, insulin action, or both. The disease has a major impact on public health and contributes to high morbidity and mortality rates in many countries. Prevention and early detection are essential to reduce the adverse effects of this disease. This study aims to analyze and apply machine learning algorithms in detecting diabetes mellitus, focusing on the use of XGBoost and LightGBM algorithms. The dataset used in this study includes various features related to diabetes risk factors, such as age, gender, body mass index (BMI), hypertension, smoking history, and HbA1c and blood glucose levels. Preprocessing was performed to clean and balance the data using the SMOTE-Tomek technique. Next, the model was built and evaluated using the K-Fold cross-validation method to measure the accuracy and stability of the model. The results showed that the XGBoost model achieved 97.31% accuracy, while the LightGBM model produced 97.26% accuracy. Combining the two models through blending techniques resulted in an accuracy of 97.51%, indicating that the combination of models can improve prediction performance. This study shows the great potential of machine learning algorithms, especially XGBoost and LightGBM, in detecting diabetes mellitus accurately and efficiently. Hopefully, the results of this study can contribute to the development of decision support systems for more effective early diagnosis of diabetes