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Group Formation Using Multi Objectives Ant Colony System for Collaborative Learning Fahmi, Fitra Zul; Nurjanah, Dade
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Collaborative learning is widely applied in education. One of the key aspects of collaborative learning is group formation. A challenge in group formation is to determine appropriate attributes and attribute types to gain good group results. This paper studies the use of an improved ant colony system (ACS), called Multi Objective Ant Colony System (MOACS), for group formation. Unlike ACS that transforms all attribute values into a single value, thus making any attributes are not optimally worth, MOACS tries to gain optimal values of all attributes simultaneously. MOACS is designed for various combinations of attributes and can be used for homogeneous, heterogeneous or mixed attributes. In this paper, sensing/intuitive learning styles (LSSI) and interests in subjects (I) are used in homogeneous group formation, while active/reflective learning style (LSAR) and previous knowledge (KL) are used for heterogeneous or mixed group formation. Experiments were conducted for measuring the average goodness of attributes (avgGA) and standard deviation of goodness of attributes (stdGA). The objectives of MOACS for homogeneous attributes were minimum avgGA and stdGA, while those for heterogeneous attributes were maximum avgGA and minimum stdGA. As a conclusion, MOACS was appropriate for group formation with homogeneous or mixed.

Sistem Keamanan Rumah Sekunder Berbasis Multimedia Message Service (MMS) Maman Abdurohman; Johan NMP Simatupang; Dade Nurjanah
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2006
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Pada Penelitian ini dibangun sebuah sistem keamanan berbasis MMS. Sistem yang dibangun mengintegrasikan layanan SMS dan MMS yang disediakan oleh operator seluler dengan komputer, kamera web dan sensor infra merah untuk menyediakan sebuah sistem keamanan yang efisien dan praktis. Efisien dalam kebutuhan kapasitas penyimpanan dan praktis dalam pengoperasian.Untuk pembangunan sistem digunakan tools bantuan seperti Now SMS/MMS Gateway untuk menerima dan mengirimkan SMS/MMS, Active Port Pro untuk pengontrolan sensor infra merah, dan ActiveX VideoGrabber untuk melakukan capture gambar dan dokumentasi video. Metode kuesioner digunakan untuk melakukan pengujian kualitatif terhadap kepraktisan dari sistem yang dibangun.Pengujian dilakukan dengan membandingkan ukuran rata-rata file dari sistem yang dibangun dengan ukuran file dari sistem CCTV secara umum. Diperoleh kesimpulan bahwa sistem yang dibangun dapat meminimalkan kebutuhan kapasitas penyimpanan. Hasil pengujian kualitatif menunjukkan bahwa 93% responden mengatakan sistem yang dibangun dapat mendukung sistem keamanan utama yang sudah ada dan 100% responden mengatakan sistem praktis dalam pengoperasian.Kata kunci: CCTV, SMS, MMS, GPRS

Analisis dan Implementasi pendekatan Hybrid untuk Sistem Rekomendasi Pekerjaan dengan Metode Knowledge Based dan Collaborative Filtering Sari Rahmawati; Dade Nurjanah; Rita Rismala
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 2 (2018): September, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.2.210

Mencari pekerjaan secara online dapat menjadi kendala tersendiri baik pada pada pelamar pekerjaan maupun pada perusahaan yang mencari karyawan. Saat ini banyak pelamar dan perusahaan lebih memilih menggunakan situs rekruitasi online dibandingkan mencari dengan menggunakan mesin pencari. Recommender system menjadi salah satu kelebihan dari website rekruitasi karena website menyimpan informasi profil pekerja lalu memberikan rekomendasi sesuai dengan data yang mereka dapatkan. Pada penelitian ini penulis membuat hybrid recommender system dengan menggabungkan dua teknik yaitu knowledge based recommender system yang akan merekomendasikan pekerjaan berdasarkan profil user, kualifikasi pekerjaan dan pengaruh dari user lain yang akan memberikan rekomendasi pekerjaan berdasarkan user lain yang memiliki kesamaan. Hasil prediksi dari 2 metode itu akan digabungkan berdasarkan social aperture yang diberikan. Berdasarkan hasil pengujian hybrid recommender system memberikan hasil terbaik untuk memprediksi interaksi dan memberikan rekomendasi berdasarkan hasil RMSE dan f1 score.

Apriori Association Rule for Course Recommender system Fakhri Fauzan; Dade Nurjanah; Rita Rismala
Indonesia Journal on Computing (Indo-JC) Vol. 5 No. 2 (2020): September, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.2.434

Until recently, recommender systems have been applied in learning, such as to recommend appropriate courses. They are based on users’ ratings, learning history, or curriculum that provide relationship between courses. The last approach, however, can’t be applied to Massive Open Online Courses (MOOCs) that don’t maintain such information. Hence, course recommender systems for MOOCs must be based on other learners’ experience. This paper discusses such recommender systems. We apply Apriori Association Rule and the case study used in this study is the Canvas Network dataset and the HarvardX-MITx dataset. The proposed recommender system consists of a pre-processing to normalize data and reduce anomalous data, data cleaning to handle empty data, K-Modes clustering to group users, grouping registration transactions for filtering user registration transaction, and finally, rule formation using the Apriori Association Rule. The performance of the association rules obtained, a lift ratio evaluation metric is used. The experiments results show the best parameters in this study are 0.01 for minimum support and 0.6 for minimum confidence. With these two parameters, the number of rules and the average lift ratio value on the Canvas Network dataset are 110 rules and 19.055, while the HarvardX-MITx dataset is 48 rules and 3.662.

Group Formation Using Multi Objectives Ant Colony System for Collaborative Learning Fitra Zul Fahmi; Dade Nurjanah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Collaborative learning is widely applied in education. One of the key aspects of collaborative learning is group formation. A challenge in group formation is to determine appropriate attributes and attribute types to gain good group results. This paper studies the use of an improved ant colony system (ACS), called Multi Objective Ant Colony System (MOACS), for group formation. Unlike ACS that transforms all attribute values into a single value, thus making any attributes are not optimally worth, MOACS tries to gain optimal values of all attributes simultaneously. MOACS is designed for various combinations of attributes and can be used for homogeneous, heterogeneous or mixed attributes. In this paper, sensing/intuitive learning styles (LSSI) and interests in subjects (I) are used in homogeneous group formation, while active/reflective learning style (LSAR) and previous knowledge (KL) are used for heterogeneous or mixed group formation. Experiments were conducted for measuring the average goodness of attributes (avgGA) and standard deviation of goodness of attributes (stdGA). The objectives of MOACS for homogeneous attributes were minimum avgGA and stdGA, while those for heterogeneous attributes were maximum avgGA and minimum stdGA. As a conclusion, MOACS was appropriate for group formation with homogeneous or mixed.

Cyberbullying Detection on Twitter using Support Vector Machine Classification Method Ni Luh Putu Mawar Silveria Putri Waisnawa; Dade Nurjanah; Hani Nurrahmi
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): Maret 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Bullying is when someone or a group of individuals is continuously attacked. Because of the advancement of the internet, it has become very easy for society to engage in harmful acts of bullying by attacking a person or group of people who can hurt the victim, this is known as cyberbullying. Twitter is a social media platform that may be used by the society to share information and can also be used to perpetrate cyberbullying actions by sending messages (tweets) that addressed to the victims. This final project was developing a system to detect cyberbullying on Twitter. The system uses the Support Vector Machine method to classify whether the tweets that are shared include cyberbullying or not. In addition, this research also uses Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram feature extraction for data that has gone through the pre-processing stage. In collecting data, the author crawled tweets based on the keywords 'jelek', 'bodoh', 'goblok', 'brengsek', 'bangsat', 'memalukan', 'laknat', 'bacot' and 'pelacur'. The best performance results of the research is 76.2% accuracy, 73.2% precision, 78.2% recall and 75.6% F1-Score generated by the RBF kernel with a total of n=1

Recommendation System from Microsoft News Data using TF-IDF and Cosine Similarity Methods Gisela Yunanda; Dade Nurjanah; Selly Meliana
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): Juni 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

The rapidly growing information causes information overload, so news portals publish information massively. Readers need time to search and read more news, but the time relevance of news wears off quickly. A recommendation system is needed that can recommend news according to the preferences of readers. This study recommends news using the TF-IDF method. TF-IDF gives weight to each word in the news title, and then looks for similarity between stories using cosine similarity. To prove the accuracy of whether the system recommendation results were actually clicked by the reader, the recommendation results were matched with the reader's news history on the online news portal Microsoft News using a hit-rate. The hit-rate result in this study was 80.77%.

Hate Speech Detection on Twitter through Natural Language Processing using LSTM Model Cepthari Ningtyas Arbaatun; Dade Nurjanah; Hani Nurrahmi
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): Desember 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2718

Currently, social media is a place to express opinions. This opinion can be positive or negative. However, lately, the opinion that often appears is a negative opinion, such as hate speech. Hate speech is often found on social media, such as malicious comments intended to insult individuals or groups. Based on WeAreSocial data in 2021, one of the most used social media platforms in Indonesia is Twitter, with 63.6% of users. According to the Indonesia National Police, hate speech cases were more dominant during the period from April 2020 to July 2021. Therefore, efforts are needed to identify hate speech on the Twitter platform. One way to detect hate speech is by using deep learning. In this research, we use a deep learning model of Long Short-Term Memory (LSTM) with word embedding. FastText and Global Vector (GloVe) is the word embeddings that we use as input for word representation and classification. FastText embeddings make use of subword information to create word embeddings and GloVe embeddings using an unsupervised learning method trained on a corpus to generate distributional feature vectors. From the evaluation results on the experimental model, LSTM-FastText using random oversampling has an advantage with an F1-score of 89.91% compared to LSTM-GloVe to obtain an F1-score of 82.14%.

Measuring and Mitigating Bias in Bank Customers Data with XGBoost, LightGBM, and Random Forest Algorithm Berliana Shafa Wardani; Siti Sa'adah; Dade Nurjanah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25768

To retain its clients, the Portuguese banking institution conducts direct marketing in the form of phone calls to conduct marketing so that clients subscribe to the bank's term deposit. The data used is named bank customers data. Important client features are considered in the acquisition process. This research was conducted with bank customers data from Portuguese banking institution which implements agent acquisition. With a large number of data on bank customers, it can lead to a diversity of data which allows the results of agent acquisition to be unfair. With this, a bias detection and mitigation algorithm are needed to achieve fairness. AI fairness 360 (AIF 360) is a toolkit that provided a bias detection and mitigation algorithm. The bias mitigation algorithm in AIF 360 is divided into three processes, namely reweighing and learning fair representation at the pre-processing stage, prejudice remover and adversarial debasing at the in-processing stage, and equalized odds and reject option classification at the post-processing stage. The output of this study is a comparison of the calculation of bias detection with disparate impact (DI) and statistical parity differences (SPD) before and after mitigation. The adversarial debiasing algorithm performed best than others with 0.943 of DI, -0.004 of SPD, and also increased the 0.015% of the AUC score. Conducting this research can help the prediction of client’s term deposits in Portuguese banking institution more fairly.

Implementasi Dan Analisis Ripple-down Rule Pada Diagnosa Penyakit Jantung Adiyaka Niastya Ihsan Maulana; Dade Nurjanah
eProceedings of Engineering Vol 2, No 2 (2015): Agustus, 2015
Publisher : eProceedings of Engineering

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Abstractâ€”RDR (ripple-down rules) adalah algoritma suatu metode pengakuisisi basis pengetahuan yang digunakan pada sistem pakar. Algoritma ini dapat menentukan hasil klasifikasi sekaligus memperbarui basis pengetahuan dari sistem pakar yang menggunakannya. Karena itu paper ini bertujuan untuk mengimplementasikan metode RDR ini pada sistem pakar untuk mendiagnosa penyakit jantung berdasarkan gejala-gejala yang ada. Hasil dari pengujian yang telah dilakukan dapat disimpulkan sistem pakar dengan algoritma RDR ini dapat memperbarui basis pengetahuannya tanpa memerlukan Knowledge Engineer dan hasil dari diagnosanya tergantung pada urutan masukan kasus yang diterima sistem pakar dan bagaimana pakar (user) menentukan hasil diagnosa pakar. Sistem pakar yang menggunakan algoritma RDR ini efektifitasnya akan meningkat seiring dengan banyaknya kasus yang diterima oleh sistem. Keywordsâ€”RDR (Ripple-down Rules), expert system, medical diagnosis

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