Articles
<![CDATA[Metode Jaringan Syaraf Tiruan Untuk Prediksi Performa Mahasiswa Pada Pembelajaran Berbasis Problem Based Learning (PBL) ]]>
<![CDATA[Badieah, Badieah]]>;
<![CDATA[Gernowo, Rachmat]]>;
<![CDATA[Surarso, Bayu]]>
<![CDATA[ JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 6, No 1 (2016): Volume 6 Nomor 1 Tahun 2016 ]]>
Publisher : <![CDATA[Universitas Diponegoro ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (837.567 KB)
|
DOI: 10.21456/vol6iss1pp46-58
<![CDATA[In order to improve academic quality in higher education, students’ performance evaluation is becoming important. To prevent increasing failure rate in the course, we need a system that is capable of predicting student’s performance in the end of the course. The research used several factors that are considered to affect students' performance on Problem Based Learning (PBL), such as students’ demography, students’ prior knowledge and group heterogeneity.  The method used in the study was Artificial Neural Network (ANN) with backpropagation training algorithm. Total 8 neurons were used as inputs for ANN which were obtained from gender variable (2 neurons), age variable (1 neuron), students’ average knowledge variable (1 neuron), students’ average skill variable (1 neuron) and group heterogeneity variable (3 neurons). Several different ANN architecture were tested in the study using 2, 7 and 12 hidden neurons respectively. Each architecture was trained using various different training parameters in order to find the best ANN architecture. Dataset used in the research were obtained from Academic Information System in Faculty of Dentistry Unissula which contained Adult and Elderly Diseases Course’s participants from year 2009 to 2013. The ANN output were numeric values which represented students’ performance in Adult and Elderly Diseases Course. The output of this study is a system that is able to predict the student performance in block course. The result shows that using 7 hidden neurons in the network combining with 0.5 ,0.1 and 9000 for learning rate, momentum and epoch respectively, were the best ANN architechture and parameters in the study. The MSE obtained from validation test was 0,011926 with correlation coefficient (R) 0,796879. The prediction system are expected to help faculty and academic evaluation team to conduct actions to improve student’s academic performance and prevent them from failure in the course. ]]>
<![CDATA[Penerapan Standar Metadata Dublin Core (DC) dan Open Archive Initiatif (OAI) di Fakultas Teknologi Industri UNISSULA ]]>
<![CDATA[Alfiah Nurul Fatimah Intan Pertiwi]]>;
<![CDATA[Imam Much Ibnu Subroto]]>;
<![CDATA[Badieah Assegaf]]>
<![CDATA[ TRANSISTOR Elektro dan Informatika Vol 2, No 1: 2017 ]]>
Publisher : <![CDATA[Universitas Islam Sultan Agung ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (802.415 KB)
|
DOI: 10.30659/ei.2.1.21-30
<![CDATA[Perpustakaan Fakultas Teknologi Industri (FTI) merupakan perpustakaan yang menyimpan hardcopy maupun softcopy Tugas Akhir / Tesis mahasiswa Teknik Elektro, Teknik Industri, Teknik Informatika. Dalam penyimpanan data tersebut masih dilakukan secara konvensional. Penyimpanan softcopy file masih menggunakan CD / kaset. Tidak adanya sistem penyimpanan digital tersebut menyulitkan pihak dosen maupun mahasiswa untuk melihat penelitian apa saja yang telah dibuat sebelumnya untuk dijadikan referensi atau patokan. Sehingga kemungkinan terjadinya plagiarisme judul atau tema penelitian yang sama sangat besar. Untuk mengurangi penggunaan CD / Kaset tersebut dibangun sebuah sistem penyimpanan digital atau biasa disebut sistem repositori. Sistem repositori perpustakaan FTI bertujuan pula untuk pertukaran atau (sharing) metadata. Repositori ini menggunakan metadata Dublin Core untuk melakukan sharing metadata tersebut. Metadata Dublin Core merupakan standar untuk pertukaran metadata antar repositori, misalnya pertukaran metadata antar fakultas di Unissula atau dengan perpustakaan pusat Unissula. Sistem repositori ini menggunakan OAI-PMH yang merupakan protokol yang dibangun dengan basis dari elemen-elemen Dublin Core dengan beberapa penambahan fitur. Selain tujuan di atas, protokol ini memungkinkan tukar menukar metadata antar dua atau lebih sistem. Dengan adanya sistem tersebut, penyimpanan file Tugas Akhir dikelola di dalam sistem dengan baik dan perpustakaan dapat melakukan sharing metadata antar repositori yang ada di UNISSULA.]]>
<![CDATA[Machine Learning Approaches on External Plagiarism Detection ]]>
<![CDATA[Imam Much Ibnu Subroto]]>;
<![CDATA[Ali Selamat]]>;
<![CDATA[Badieah Assegaf]]>
<![CDATA[ Journal of Telematics and Informatics Vol 4, No 2: September 2016 ]]>
Publisher : <![CDATA[Universitas Islam Sultan Agung ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (579.768 KB)
|
DOI: 10.12928/jti.v4i2.
<![CDATA[External plagiarism detection is a technique that refers to the comparison between suspicious document and different sources. External plagiarism models are generally preceded by candidate document retrieval and further analysis and then performed to determine the plagiarism occurring. Currently most of the external plagiarism detection is using similarity measurement approaches that are expressed by a pair of sentences or phrase considered similar. Similarity techniques approach is more easily understood using a formula which compares term or token between the two documents. In contrast to the approach of machine learning techniques which refer to the pattern matching and cannot directly comparing token or term between two documents. This paper proposes some machine learning techniques such as k-nearest neighbors (KNN), support vector machine (SVM) and artificial neural network (ANN) for external plagiarism detection and comparing the result with Cosine similarity measurement approach. This paper presented density based that normalized by frequency as the pattern. The result showed that all machine learning approach used in this experiment has better performance in term of accuracy, precision and recall.]]>
<![CDATA[Student Academic Performance Prediction on Problem Based Learning Using Support Vector Machine and K-Nearest Neighbor ]]>
<![CDATA[Badieah Assegaf]]>
<![CDATA[ Journal of Telematics and Informatics Vol 5, No 1: March 2017 ]]>
Publisher : <![CDATA[Universitas Islam Sultan Agung ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (461.185 KB)
|
DOI: 10.12928/jti.v5i1.22-28
<![CDATA[Academic evaluation is an important process to know how well the learning process was conducted and also one of the decisive factors that can determine the quality of the higher education institution. Though it usually curative, the preventive effort is needed by predicting the performance of the student before the semester begin. This effort aimed to reduce the failure rate of the students in certain subjects and make it easier for the PBL tutor to create appropiate learning strategies before the tutorial class begin. The purpose of this work is to find the best data mining technique to predict student academic performance on PBL system between two data mining classification algorithms. This work applied and compared the performance of the classifier models built from Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). After preprocessed the dataset, the classifier models were developed and validated. The result shows that both algorithms were giving good accuracy by 97% and 95,52% respectively though SVM showing the best performance compared to KNN in F-Measure with 80%. The further deployment is needed to integrate the model with academic information system, so that academic evaluation can be easily done.]]>
<![CDATA[Sentiment Analysis of Indonesian Figure using Support Vector Machine ]]>
<![CDATA[Suharyo Herwasto]]>;
<![CDATA[Imam Much Ibnu Subroto]]>;
<![CDATA[Badieah Assegaf]]>
<![CDATA[ Journal of Telematics and Informatics Vol 6, No 3: September 2018 ]]>
Publisher : <![CDATA[Universitas Islam Sultan Agung ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.12928/jti.v6i3.230-237
<![CDATA[On the political year 2018 will be mutually popping reverberated figures for Indonesian presidential candidate 2019. The figures recognition process generally are now using social media, so it would appear the opinions of social media users. Opinions that appeared not only contain positive and negative polarity, but also contain a sentence of subjective and objective. By using a machine learning algorithm, namely Support Vector Machine, made sentiment analysis. The results of the analysis of this sentiment more optimally use the kernel Linear with the F-Measure of Polarity 68%, 68%, 63%, and the F-Measure Subjectivity 73%, 77%, 75% for each figure Anies Baswedan, Joko Widodo, and Prabowo Subianto.]]>
<![CDATA[Forming Heterogeneous Group in Cooperative Learning Process using Partitioning Around Medoids (PAM) and Equitable Distribution ]]>
<![CDATA[Imam Much Ibnu Subrotto]]>;
<![CDATA[Badieah Badieah]]>;
<![CDATA[Wardianto Eko Saputra]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (304.391 KB)
|
DOI: 10.11591/eecsi.v4.1097
<![CDATA[Selection of methods will greatly impact in learning process. One of the methods commonly applied are Cooperative learning. Cooperative learning is one of many learning techniques to improve the performance of students in the academic literature. Moreover, the heterogeneity in study group’s academics can improve performance, but only partially implementing cooperative learning in a group of heterogeneous formations. The problem faced in this type is the process of forming group of students into a heterogeneous group and inter-group quality is relatively equal or balanced. In this study, the authors aimed to provide intelligent solutions in the distribution group based on the value (The value of achievement on related subjects) and personality traits of each student in the determination of the performance of students are using the algorithm clustering Partitioning Around Medoids (PAM) in consideration of the value of measurement Euclidean Distance (ED) and the equitable distribution to form heterogeneous groups based on their level of heterogeneity in Measured with Goodness of Heterogeneity in Group (GH) and the rate of coefficient variation (CV) in same group or between groups with groups and equitable distributions on college campuses.]]>
<![CDATA[Implementation of Least Mean Square Adaptive Algorithm on Covid-19 Prediction ]]>
<![CDATA[Sri Arttini Dwi Prasetyowati]]>;
<![CDATA[Munaf Ismail]]>;
<![CDATA[Badieah Badieah]]>
<![CDATA[ JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022 ]]>
Publisher : <![CDATA[Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (1238.009 KB)
|
DOI: 10.30595/juita.v10i1.11963
<![CDATA[This study used Corona Virus Disease-19 (Covid-19) data in Indonesia from June to August 2021, consisting of data on people who were infected or positive Covid-19, recovered from Covid-19, and passed away from Covid-19. The data were processed using the adaptive LMS algorithm directly without pre-processing cause calculation errors, because covid-19 data was not balanced. Z-score and min-max normalization were chosen as pre-processing methods. After that, the prediction process can be carried out using the LMS adaptive method. The analysis was done by observing the error prediction that occurred every month per case. The results showed that data pre-processing using min-max normalization was better than with Z-score normalization because the error prediction for pre-processing using min-max and z-score were 18% and 47%, respectively.]]>
<![CDATA[Implementation of RESTful Web Service on Indonesian’s Integrated Breastfeeding Donor Information System ]]>
<![CDATA[Badieah Badieah]]>;
<![CDATA[Ahmad Mujib]]>;
<![CDATA[Muna Yastuti Madrah]]>;
<![CDATA[Andi Riansyah]]>;
<![CDATA[Nur Muhammad Syaifuddin]]>
<![CDATA[ Sistemasi: Jurnal Sistem Informasi Vol 11, No 2 (2022): Sistemasi: Jurnal Sistem Informasi ]]>
Publisher : <![CDATA[Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (1466.629 KB)
|
DOI: 10.32520/stmsi.v11i2.1797
<![CDATA[The golden period is a term used to describe the importance of the first 1000 days of human life. Nutritional intake in children at this time becomes very important because malnutrition in this period will cause disturbances in child development. To prevent this risk, the intake of breast milk is necessary for babies at least in the first 6 months of life. However, there are many internal and external factors that can affect a baby not being able to receive it. One solution that commonly used to share breast milk amongst mothers. A mother can share breast milk directly through personal relations or through human milk bank agencies. The implication of the problem of sharing human milk in Muslim societies is the occurrence of kinship relationship between the child and his milk mother that change the status of mahram and the prohibition of marriage between breast milk children and biological children of the donor because the two children's status changed to breast milk siblings. Referring to these conditions, we designed an integrated information system prototype by integrating breast milk donor data obtained from human milk banks throughout Indonesia. Interoperability problems during data integration process are overcome by implementing a RESTful API as a web service. The output of this information system is the issuance of milk-kinship certificates given to donors and recipients as evidence of donors as well as become a token that there is a mahram bond between donors and recipients. A milk-kinship certificate can prevent marriage between milk-kinship siblings, especially in Muslim communities.]]>
<![CDATA[The Policy Integration Concept of The Mahram Relationship on Nurseling Study on Nurseling Practices in Semarang, Central Java-Indonesia ]]>
<![CDATA[Muna Madrah]]>;
<![CDATA[Badieah]]>;
<![CDATA[Andi Riansyah]]>;
<![CDATA[Alamsyah Alamsyah]]>;
<![CDATA[Dewi Ayu Fatmawati]]>
<![CDATA[ Al-Ihkam, Jurnal Hukum dan Pranata Sosial Vol 17 No 2 (2022) ]]>
Publisher : <![CDATA[Faculty of Sharia IAIN Madura collaboration with The Islamic Law Researcher Association (APHI) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.19105/al-lhkam.v17i2.6316
<![CDATA[Giving breast milk is becoming more popular, whether directly or mediated by organizations supporting nursing donors. In Muslim communities, donating breast milk has implications for the relationship between the child and the nursing mother, including the mother's husband and biological children. The relationship is called the mahram relationship. Mahram in Islam is essential because it clarifies the child's lineage and the relationship between the family of the nursing mother. The objectives of this study are 1). to describe the breastfeeding donor in Semarang; 2). to describe community awareness of its implication; and 3). questions whether any policy regulates the recording of breastfeeding donor practices to track mahram relationships. This is qualitative research by exploring information from primary sources in Semarang District. Furthermore, the data, also, is gathered from relevant policy documents to strengthen argumentation and analysis. The results indicate that breastfeeding donor policies already exist in Semarang, but each institution associated with various policies moves independently in its implementation. There is no policy regarding recording the mahram relations. The absence of a clear and integrated policy has led to a tendency for people to practice breastfeeding independently without the need to carry out official recording procedures. The research offers a cross-sectoral integrated policy concept aiming to fulfill children's rights to the best food in their early life while having the right to know the clarity of their lineage.]]>
<![CDATA[Rancang Bangun Aplikasi Klasifikasi Topik Artikel Ilmiah Bahasa Indonesia Menggunakan Metode Support Vector Machine ]]>
<![CDATA[Sam Farisa Chaerul Haviana]]>;
<![CDATA[Badieah Badieah]]>;
<![CDATA[Ghozi Fidaul Haq]]>
<![CDATA[ Journal of Applied Science and Technology Vol 3, No 01 (2023): Januari 2023 ]]>
Publisher : <![CDATA[Universitas Islam Sultan Agung ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30659/jast.3.01.%p
<![CDATA[Scientific articles were wrote as result of the research process that follow agreed rules, methods, and systematics so that the fact can be accounted for. Those scientific articles or publications were commonly available in the internet as indexed list. One of the biggest source of publication indexed in SINTA (Science and Technology Index) of Ministry of Education, Culture, Research and Technology of Indonesia. According to SINTA, the number of Indonesian publications continues to increase over years since 2017. Because of this increasing number of publications, the need of managing those documents is emerging. The management of published document data would be very difficult to do manually, including grouping or classifying documents based on the research topic. This become the background of this research on how to classify the articles topic automatically. This research utilizing support vector machine classifier to achieve the solution. After conducting research using 600 documents, we successfully classify the topic of Indonesian scientific article documents using the support vector machine method with a 94% accuracy, 95% precision, and 94% recall.]]>
