UIN (Universitas Islam Negeri) Sunan Kalijaga, Yogyakarta: E-Journal Fakultas Sains dan Teknologi
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Kapsul “BILAKU” (Biji Labu Kuning) : Inovasi Agen Fitoesterogen sebagai AlteKAPSUL “BILAKU” (BIJI LABU KUNING) : INOVASI AGEN FITOESTEROGEN SEBAGAI ALTERNATIF PANGAN PRODUK PADA WANITA POST MENSTRUAL rnatif Pangan Produk pada Wanita Post Menstrual
No full textMasa lansia pada wanita merupakan masa puncak perubahan hormonal yang ditandai berhentinya menstruasi atau disebut menopause. Menopause merupakan terjadinya penurunan produksi hormon estrogen dan progesterogen dari indung telur. Penurunan estrogen pada masa menopause menimbulkan banyak keluhan pada wanita yang berdampak pada keadaan fisik dan kejiwaannya. Solusi dalam mengurangi permasalahan menopause salah satunya adalah dengan Terapi Sulih Hormon (TSH). Namun, penggunaan TSH dengan estrogen (TSH tunggal) maupun kombinasi estrogen-progesteron (TSH ganda) dalam jangka waktu panjang dapat menyebabkan tromboemboli, kanker payudara, penyakit kandung empedu, hingga stroke. Penelitian ini bertujuan untuk mengkaji dan menganalisis permasalahan post menstrual yang terjadi pada semua wanita. Pemanfaatan biji labu kuning (BILAKU) di masyarakat masih tergolong rendah, padahal kandungan senyawa aktif dan antioksidan dapat bermanfaat untuk pengobatan pada wanita pasca menopause. Biji labu kuning memiliki aktivitas antioksidan yang cukup tinggi diantaranya vitamin C, vitamin E, betakaroten, dan senyawa fenolik, karena kandungan senyawa antioksidannya yang cukup tinggi maka mampu menangkap radikal bebas, serta kandungan senyawa fitoesterogen yang mampu mengurangi dampak post menstrual. Untuk mengurangi dampak dari pemakaian obat sintetis solusi yang ditawarkan yakni rancangan inovasi baru berbahan pangan lokal yang dapat dijadikan sebagai pangan fungsional bagi wanita yang sudah mengalami masa menopause yakni dengan pembuatan kapsul biji labu kuning (BILAKU). Tahapan penelitian yang dilakukan yakni tahap pengumpulan sampel, tahap ekstraksi sampel, dan tahap pembuatan kapsul. Kapsul biji labu kuning (BILAKU) memiliki kandungan kadar air sebesar maks.5% sehingga dalam kondisi kering bisa dikonsumsi langsung dan praktis disimpan dalam jangka waktu yang lama. Kapsul biji labu kuning (BILAKU) dapat menjadi terobosan baru dari pemanfaatan biji labu kuning untuk mengurangi efek samping dari penggunaan obat-obatan sintetis
NARRATIVE REVIEW: POTENSI ORGAN-ON-CHIPS (OOC) SEBAGAI PENGGANTI HEWAN DALAM EVALUASI PRA-KLINIS EFEKTIVITAS DAN TOKSISITAS OBAT
No full textAbstrak. Evaluasi pra-klinis perlu dilakukan untuk meminimalisir kegagalan penemuan dan pengembangan obat baru. Pada fase ini calon obat akan diujikan pada hewan eksperimen. Hanya saja hewan eksperimen memiliki keterbatasan untuk meniru sistem yang kompleks dari tubuh manusia. Selain itu penggunaan hewan eksperimen sudah tidak etis untuk mengorbankan hewan demi kepentingan manusia. Oleh karena itu Organ-on-Chips (OOC) dibuat untuk menyediakan subjek pengujian buatan yang menyerupai tubuh manusia dalam setiap aspek. OOC merupakan model sistem mikrofluida humanoid yang menampung sel dan jaringan organ hidup. OOC dapat menghasilkan data efektivitas dan profil toksisitas obat lebih akurat apabila dibandingkan dengan uji konvensional menggunakan hewan eksperimen. Penelitian ini merupakan penelitian kualitatif yang menggunakan metode narrative review berdasarkan sumber data sekunder. Hasil penelitian ini berupa ulasan etika penggunaan hewan eksperimen dari perspektif agama Islam yang didasarkan pada Alquran dan hadits. Penelitian juga menilik OOC dari segi potensi, fabrikasi, implementasi dan perkembangan riset terkait.
Kata kunci: in vitro-in vivo, kepedulian etika, mikrofluidik, penemuan obat, dan uji pra-klinis.
Abstract. Pre-clinical evaluation needs to be done to minimize the failure of new drug discovery and development. In this phase, the drug candidate will be tested on experimental animals. But experimental animals have limitations to imitate the complex systems of the human body. In addition, the use of experimental animals is unethical to sacrifice animals for the benefit of humans. Therefore Organ-on-Chips (OOC) were created to provide artificial test subjects that resemble the human body in every aspect. OoCs are models of humanoid microfluidic systems that accommodate living cells and tissues. OOC can produce more accurate data effectiveness and drug toxicity profiles when compared to conventional tests using experimental animals. This research is a qualitative research that uses a narrative review method based on secondary data sources. The results of this study are in the form of a review of the ethics of experimental animals from the perspective of Islam, which is based on the Quran and hadith. This research also looks at OOC in terms of potential, fabrication, implementation and development of related research.
Keywords: in vitro-in vivo, ethical concerns, microfluidic, drug discovery, and pre-clinical trials
Evaluation of the Maturity Level of Information Technology Security Systems Using KAMI Index Version 4.2 (Case Study: Islamic Boarding Schools in Yogyakarta Special Region Province)
No full textThe development of information technology worldwide has changed very rapidly. There has been a data theft on the information system belonging to one of the most prominent Islamic Boarding Schools in the Yogyakarta area. Thus, special attention is needed to evaluate information technology security using the Information Security Index version 4.2. The research methods include extracting information, literature study, data collection, data validation, data analysis, and recommendations. The evaluation results are at the basic framework fulfilment level with a value of 343; the electronic system category has a low status with a value of 15 and 5 improvements; the governance category, the risk management category, the framework category, the asset management category, and the information security technology category, have a maturity level II status with 12, five, eight, four, and eight recommendations respectively, while the supplement category for third party security areas with a value of 60%, securing cloud infrastructure services 56% and protecting personal data 61% with 14 recommendations
Analysis of Factors Affecting the Students’ Acceptance Level of E-Commerce Applications in Yogyakarta Using Modified UTAUT 2
No full textYogyakarta is listed as the region with the highest number of residents engaging in e-commerce transactions. A total of 10.2% of the population are active e-commerce sellers, while 16.7% belong to the buyer category. Research by IDN Times showed that e-commerce application users have been dominated by students, with a percentage of 44.2%. The purpose of this study is to analyze the factors that influence students’ level of acceptance of e-commerce applications in Yogyakarta using the modified UTAUT 2. This is quantitative research with multiple linear regression models using SPSS software version 25 with a sample size of 303 people. Data analysis in this study was conducted in a few steps, including descriptive analysis, validity test, reliability test, classical assumption test and hypothesis testing. The results of this study indicate that the student’s level of acceptance of e-commerce applications is within good criteria. The variables that have a positive effect on the behaviour intention (BI) are performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), habit (HB), price value (PV), perceived risk (PR), perceived security (PS), and trust (TR) are variables that negatively affect the variable behaviour intention (BI). All independent variables affect the dependent variable or behaviour intention (BI) with a total of 63.3% and the difference with a total of 36.7% is caused by other factors not examined by the researcher
Regresi Logistik Multinomial untuk Prediksi Kategori Kelulusan Mahasiswa
No full textStudents must meet certain goals to earn a degree but can extend their time at university or drop out (DO). The problem of dropping out of students has become an important issue for tertiary institutions to ensure the success or graduation of students and reduce dropouts. DO can affect the accreditation of the tertiary institution. The quality of higher education institutions in Indonesia is measured based on accreditation from the National Accreditation Board for Higher Education or BAN-PT. One of the main standards measured is the Quality of Students and Graduates. The quality of educational accreditation is measured by the percentage of student graduation and the university\u27s strategy to retain students. To predict student graduation based on graduation time categories, researchers collected academic data from students in 2012-2018 at the Informatics Engineering Study Program, State Islamic University of Maulana Malik Ibrahim Malang. The variables used as predictors are gender, type of entry pathway, and grade point average from semesters one to six. The resulting model was evaluated to obtain an accuracy value of 85.5%, a precision of 78.5%, a recall of 93.9%, and a micro f1-score of 89.8%. An accuracy value of 85.5% indicates that the system can classify properly using the logistic regression model
Analisis Bibliometrik Publikasi Isu Kebocoran Data Menggunakan VOSviewer
No full textData leakage can harm individuals who experience it, such as identity theft, financial fraud, or even physical security threats. This study aims to analyze scientific publications and research activities in data leakage using bibliometric techniques by involving quantitative analysis using a set of journals as reference sources. The literature for this study was obtained using Publish or Perish software with the keyword "data leakage" and a search result limit of 500. Furthermore, from these results, 85 journals relevant to the research topic were selected for visualization using VOSviewer software. The results showed that the highest number of data leakage publications occurred in 2021, namely 30 journals published that year. Based on the results of the bibliometric analysis conducted, it is known that there are 9 clusters based on keywords and 19 clusters based on author. The most frequently researched keywords include personal data, privacy, data leakage, legal protection, security, cryptography, encryption, and description. On the other hand, some keywords are rarely discussed in publications, namely, air transportation, electronics, and Caesar
ANALISIS FORENSIK TERHADAP KASUS CYBERBULLYING PADA INSTAGRAM DAN WHATSAPP MENGGUNAKAN METODE NATIONAL INSTITUTE OF JUSTICE (NIJ)
No full textSalah satu dampak negatif dari kemajuan teknologi informasi dan komunikasi adalah munculnya fenomena Cyberbullying. Lembaga donasi anti-bullying, Ditch The Label pada surveinya “The Annual Bullying Survey 2017”, mencatat lebih banyak anak muda yang mengalami cyberbullying di Instagram dari pada platform lain sebesar 42 persen, dengan Facebook mengikuti di belakang dengan 37 persen. Snapchat berada di peringkat ketiga dengan 31 persen, sementara WhatsApp (12 persen), Youtube (10 persen), Twitter (9 persen) dan Tumblr (2 persen) dari cyberbullying yang dilaporkan. Kejahatan yang dilakukan oleh pelaku cyberbullying tentunya akan meninggalkan barang bukti berupa bukti digital percakapan tentang kejahatan yang dilakukan oleh pelaku dan korban. Maka dari itu perlu adanya teknik digital forensik untuk perncarian bukti digital yang valid. Pada penelitian ini, peneliti membuat skenario kasus cyberbullying pada aplikasi Instagram dan Whatsapp melalui Smartphone. Tujuan dari penelitian ini, untuk mengetahui cara dalam melakukan mobile forensics dengan menerapkan metode NIST Special Publication 800-101 Revision 1 dan mengetahui hasil analisis dari aplikasi forensik MOBILEdit, Autopsy dan FTK Imager dalam pencarian bukti digital cyberbullying pada aplikasi Instagram dan Whatsapp. Hasil penelitian menunjukkan bahwa bukti digital berhasil ditemukan hampir keseluruhan data sesuai skenario menggunakaan aplikasi Autopsy dan FTK Imager, dengan menggunakan physical image yang diperoleh dari ekstrak MOBILEdit dalam smartphone kondisi root. Namun, untuk FTK Imager harus mengetahui lokasi terlebih dahulu agar lebih mudah dalam pencarian data. Hasil data dari aplikasi MOBILEdit, pada Instagram untuk video dan file yang telah dihapus tidak ditemukan, sedangkan pada WhatsApp hanya ditemukan file storage-nya.
Kata Kunci : cyberbullying, instagram, whatsApp, NIJ, digital forensic.
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The negative impact of advances in information and communication technology which is increasing this year is the emergence of the Cyberbullying phenomenon. Anti-bullying charity, Ditch The Label in its survey "The Annual Bullying Survey 2017", noted that more young people experience cyberbullying on Instagram than on other platforms at 42 percent, with Facebook following behind with 37 percent. Snapchat ranked third with 31 percent, while WhatsApp (12 percent), Youtube (10 percent), Twitter (9 percent), and Tumblr (2 percent) reported cyberbullying. The criminal behavior committed by cyberbullying perpetrators will certainly leave the evidence in the form of digital evidence of conversations about crimes committed by perpetrators and victims. Therefore, it is necessary to have digital forensic techniques to search for valid digital evidence. In this study, researchers created scenarios of cyberbullying cases on Instagram and Whatsapp applications via cell phones. This study aims to find out how to carry out forensic analysis using the NIJ method and find out the results of analysis from the forensic applications MOBILedit, Autopsy, and FTK Imager in searching for digital evidence of cyberbullying on the Instagram and Whatsapp applications. The results showed that digital evidence was found in almost all of the data according to the scenario using the Autopsy and FTK Imager applications, using a physical image obtained from the MOBILedit extract in a rooted cellphone. However, the FTK imager must know the location first so that it is easier to find data. Data results from the MOBILedit application, on Instagram for deleted videos and files were not found, while on WhatsApp only storage files were found.
Keywords: cyberbullying, instagram, whatsapp, NIJ, digital forensics
DETEKSI SERANGAN LOW RATE DDOS PADA JARINGAN TRADISIONAL MENGGUNAKAN MACHINE LEARNING DENGAN ALGORITMA DECISION TREE
No full textDecision tree adalah salah satu metode yang sering digunakan dalam data mining dan machine learning untuk memprediksi hasil atau mengambil keputusan berdasarkan input yang diberikan. Algoritma ini menciptakan pohon keputusan yang terdiri dari node yang mewakili pertanyaan atau kondisi dan edge yang menghubungkan node-node tersebut. Dalam aplikasinya untuk mendeteksi serangan Low Rate DDoS (Distributed Denial of Service) pada jaringan tradisional, decision tree dapat digunakan untuk memprediksi kemungkinan terjadinya serangan Low rate DDoS berdasarkan beberapa fitur yang dianggap penting dalam mengidentifikasi serangan tersebut. Fitur-fitur tersebut bisa berupa jumlah traffic yang masuk ke jaringan, tipe traffic yang masuk, atau karakteristik traffic lainnya. Setelah fitur-fitur tersebut dikumpulkan, Decision tree dapat digunakan untuk memprediksi kemungkinan terjadinya serangan Low rate DDoS pada jaringan tradisional dengan menganalisis fitur-fitur yang dianggap penting dan membuat keputusan berdasarkan pertanyaan-pertanyaan yang sesuai. Penelitian ini bertujuan untuk menganalisis perbandingan hasil dari dua metode decision tree, yaitu algoritma Gini Index dan Entropy, untuk mendeteksi serangan low rate DDoS (Distributed Denial of Servcice) pada jaringan tradisional dengan menggunakan dataset CICIDS 2017 . Hasil analisis menunjukkan bahwa metode decision tree dengan algoritma Gini Index lebih baik dari Entropy untuk mendeteksi low rate DDoS (Distributed Denial of Servcice) pada jaringan tradisional berdasarkan nilai Accuracy, Precision , dan F1 Score, yaitu dengan nilai 99,740%, 99,113%, dan 99,231%. Namun, metode decision tree dengan algoritma Entropy lebih baik dari Gini Index berdasarkan nilai Recall, yaitu dengan nilai 99,351%.
Kata kunci: Decision tree, DDoS, Machine learning, CICIDS2017, Gini Index , Entropy
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Distributed Denial of Service (DDoS) attacks are attacks that can paralyze network traffic and services by overloading servers, network links and network devices (switches, routers, etc.) with very high network traffic. DDoS detection can be done using machine learning, one of which is using the Decision Tree algorithm. Decision Tree is a method that is often used in data mining and machine learning to predict results or make decisions based on the input provided. In its application to detect Low Rate DDoS (Distributed Denial of Service) attacks on traditional networks, decision trees can be used to predict the possibility of Low rate DDoS attacks based on several features that are considered important in identifying such attacks. These features can be the amount of traffic that enters the network, the type of traffic that comes in, or other traffic characteristics. To detect low rate DDoS attacks on traditional networks using the CICIDS 2017 dataset. The results of the analysis show that the decision tree method with the Gini Index algorithm is better than Entropy for detecting low rate DDoS (Distributed Denial of Service) on traditional networks based on Accuracy, Precision, and F1 Score, with values of 99.740%, 99.113% and 99.231%.
Keywords: Decision tree, DDoS, Machine learning, CICIDS2017, Gini Index , Entrop
Audio-Visual-Based Optical Applications as a Media to Improve Students\u27 Understanding of Concepts: A Feasibility Test
No full textOne of the learning media by utilizing device technology is realized in the form of an audio-visual physics-based physics mobile learning application called Optically. In the mobile learning application, there are various kinds of content, such as illustrated images, educational videos, sample questions, and practice questions. The research conducted has the aim of (1) producing products in the form of audio-visual physics mobile learning applications on the subject of Diffraction as a learning medium; (2) knowing the feasibility of audio-visual physics-based physics mobile learning applications as a supporting medium for students in understanding the concept of physical optics material. The research and development method uses the ADDIE model which consists of 5 stages, namely Analysis, Design, Development, Implementation, and Evaluation. However, this study is limited only to the development stage (Design). The product feasibility tester in this study was carried out by 17 people who had studied Physical Optics Physics and were studying in the field of Physics. The results of the analysis of the feasibility test of the product carried out, obtained on average on all indicators belonging to the very feasible category
A Artificial Intellegent algorithms for Tumor Disease Detection: systematic Literature Review
No full textA Tumor is a swelling in the body caused by cells that multiply abnormally. Tumors or neoplasms consisting of benign tumors and malignant tumors. Benign tumors can grow larger but do not spread to other body tissues. Malignant tumors are cancers that attack the entire body and are uncontrollable. Comparison between the cell nucleus with the cytoplasm of malignant tumors, while benign tumors are the same as normal cells. Cancer cells can develop rapidly. These cells attack and damage body tissues through the bloodstream and lymph vessels so that they can grow in new places. One way to detect tumor disease is by utilizing Artificial intelligence algorithms for tumor Disease Detection. The purpose of this paper is for the development of Artificial Intellegent algorithms for the detection of tumor Diseases and optimization of Artificial Intellegent algorithms for the detection of tumor Diseases. This research uses systematic literature review by using preferred Reporting Items for Systematic Review (PRISMA). The results of screening and selection of articles obtained 64 potential articles that have met the inclusion criteria. The results showed that with earlier detection, a person can check tumor disease earlier using the help of Artificial intelligence algorithms. The results of research on the development of Artificial intelligence algorithms for detection of tumor Diseases have found Artificial intelligence algorithms that can be used to reduce the risk of tumor disease. Optimization of Artificial Intelegency algorithms for tumor classification, performing new data processing methods such as artificial intelligence can be selected to provide the accuracy of classification and diagnosis, exploration of detection limits is a very important aspect in tumor diagnosis based on SERS, finding improved and suitable nanoparticle substrates so as to significantly improve the original Raman signal