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Sistem Pencarian Ayat Al-Quran Berdasarkan Kemiripan Ucapan Menggunakan Algoritma Soundex dan Damerau-Levenshtein Distance Puruhita Ananda Arsaningtyas; Moch. Arif Bijaksana; Said Al Faraby
Jurnal Linguistik Komputasional Vol 1 No 2 (2018): Vol. 1, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (583.631 KB) | DOI: 10.26418/jlk.v1i2.10

Abstract

Prediksi Tingkat Inflasi Di Indonesia Berbasis Jaringan Syaraf Tiruan Dan Algoritma Genetika Rita Rismala; Said Al Faraby
Indonesia Symposium on Computing Indonesian Symposium on Computing 2014/Seminar Nasional Ilmu Komputasi Teknik Informatika (SNIKTI)
Publisher : Indonesia Symposium on Computing

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Inflasi menjadi indikator yang sangat penting dalam menganalisis perekonomian negara. Oleh karena itu prediksi terhadap nilai inflasi menjadi penting agar dapat membantu pemerintah dalam mengambil kebijakan untuk menjaga stabilitas moneter dan perekonomian. Pada penelitian ini dilakukan prediksi tingkat inflasi di Indonesia dengan tidak hanya mempertimbangkan data historis inflasi, namun juga mempertimbangkan faktor-faktor lain yang mempengaruh tingkati inflasi di Indonesia. Prediksi dilakukan menggunakan Jaringan Syaraf Tiruan dengan menggunakan algoritma pembelajaran berbasis Algoritma Genetika. Hasil pengujian menunjukkan bahwa akurasi sistem dalam memprediksi nilai tingkat inflasi belum cukup baik. Namun dalam memprediksi kelas inflasi, sistem ini sudah cukup baik terutama dalam mengidentifikasi inflasi dengan kelas rendah.
DESIGN AND ANALYSIS OF UTILIZING POMDP IN ONLINE RESERVATION SYSTEM Said Al Faraby
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 2 No. 1 (2015)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (719.689 KB) | DOI: 10.33197/jitter.vol2.iss1.2015.68

Abstract

System personalization is a smart way to handle interaction with various types of customers that come with different preferences. In this project we attempt to optimize the sequential decision processes during the interaction between an online hotel reservation service and its customer. We use POMDP framework to determine the best response for the system. POMDP is suitable for this problem because it takes into account the non-observability of the customer preferences, and the uncertainty of the observations that come from the customer. We present our findings regarding the potential improvement by utilizing belief information, and the sufficient level of observation certainty in order to keep the model considerably useful. We also show the resulting policies trees with the belief information in order to clarify the intuition behind the actions planned
Multi Label Topic Classification for Hadith Bukhari in Indonesian Translation using Random Forest Adhitia Wiraguna; said al faraby; Adiwijaya Adiwijaya
Journal of Data Science and Its Applications Vol 4 No 1 (2021): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2021.4.70

Abstract

Hadith is a mandatory thing to be studied and practiced by Muslims. There are many types of teachingsthat humans can take by studying the hadith. To assist Muslims in studying the hadith, a multi labelclassification system is needed to categorize Sahih Bukhari Hadi in Indonesian translation based on threetopics, namely prohibition, advice and information. In building a text classification system, there are variousclassification methods that can be used, in this study using Random Forest (RF). The simplicity of the RFalgorithm and good ability to deal with high dimensional data, make RF a suitable method of textclassification. But, there is not widely known RF capability for the multi label classification. This study usesthe Problem Transformation approach method, namely Binary Relevance (BR) and Label Powerset (LP)to adapt RF in building a multi label classification system. The results showed that the best hamming lossperformance obtained from a system that used BR and does not use stemming which is equal to 0,0663.These results indicate that the BR method is better than the LP method in adapting the RF algorithm toperform multi label classification of hadith data. This is happened because the BR method produces aclassification model of the number of labels in the hadith data and on the other hand, the transformation ofdata from the use of LP makes the data are imbalanced.
Penerapan Question Answering System Pada Pembahasan Agama Islam Dengan Pendekatan Metode Pattern Based Rosyadi, Ramadhana; Al-Faraby, Said; Adiwijaya, Adiwijaya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 2, No 4 (2018): Oktober 2018
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v2i4.949

Abstract

Islam has 25 prophets as guidelines for human life, documents containing information about the stories of the lives of the prophets during their lifetime. This study aims to build a more specific question and answer system by generating relevant answers not in the form of documents. Question Answering System is able to overcome problems in the Question and answer system, information retrieval systems where the answers issued are correct with responses to requests submitted, not in the form of documents that may contain answers. This study uses the Pattern Based method as extracting sentence pieces which are the answers to find answers that match the patterns that have been made. The selection of datasets causes a number of questions that can be submitted to be limited to information stored in the data itself. Besides that, questions are also limited in the form of Question words that are Factoid, namely Who, when, where, what and how. Accuracy results obtained using the Pattern Based method on Question Answering System are 39.36%.
Analisis Metode Pattern Based Approach Question Answering System Pada Dataset Hukum Islam Berbasis Bahasa Indonesia Ade Iriani Sapitri; Said Al-Faraby; Adiwijaya Adiwijaya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 2, No 4 (2018): Oktober 2018
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v2i4.950

Abstract

Islamic law is a provision of the command of Allah SWT which has different laws. It takes a long time in the process of searching information manually given the many types of islamic law. From the above problems with the help of Question Answering System can solve the problem. The purpose of this study is to assist usesrs in finding the required information with input in the form of question with property category (OBJECT) What, (PERSON) Who, (LOCATION) Where, (TIME) When and (COUNT) How much. Research Question Answering System is implemented with the Pattern Based Approach method based on pattern classification. In this research we get the result of accuracy of answer equal 64,5% in every type of question category “What”,”When”,”How much”, “Who”, and “Where” with answer accuracy equal to 63,3%, 65%, 73,3%, 65% and 40%. From the accuracy results obtained that the method of Pattern Based Approach is able to be implemented in Question Answering System to solve the above problems
Pengaruh Distribusi Panjang Data Teks pada Klasifikasi: Sebuah Studi Awal Said Al Faraby; Ade Romadhony
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4259

Abstract

In text classification, there is a problem with text domain differences (cross-domain) between the data used to train the model and the data used when the model is applied. In addition to the problem of domain differences, there are also language differences (cross-lingual). Many previous studies have looked for ways how classification models can be applied effectively and efficiently in these cross-domain and cross-lingual situations. However, there is one difference that is not given special attention because it is considered not very influential, namely the difference in text length (cross-length). In this study, we further investigated the cross-length condition by creating a special dataset and testing it with various commonly used classification models. The results showed that the difference in the distribution of text length between the training data and the test data could affect the performances. Cross-length transfers from long to short texts show an average decrease in F1-scores across all models of 14%, while transfers from short to long texts give an average decrease of 9%.
Penerapan Particle Swarm Optimization Pada Feedforward Neural Network Untuk Klasifikasi Teks Hadis Bukhari Terjemahan Bahasa Indonesia Muhammad Ghufran; Adiwijaya Adiwijaya; Said Al-Faraby
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 2, No 4 (2018): Oktober 2018
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v2i4.951

Abstract

Hadith is the second source of Islamic law after Al-Qur'an and used as a guide for Muslims life. there are many hadith which has been narrated, one of them is Bukhari history. This research aims to build a model that can classify Bukhari hadith translation of Indonesian language. This topic is chosen to assist the public in understanding the meaning of the information that contained in the hadith, in the form of advocacy information, prohibitions or just information. The Backpropagation Algorithm (BP) is the general technique that used to train the Feedforward Neural Network (FNN) in classification process cause it has good accuracy for text classification. But, BP has a weakness that is relatively slow to reach convergent and stuck in local minimum. To overcome this, the Particle Swarm Optimization (PSO) algorithm is used to speed up convergence and find the minimum global value. The purpose of this test is to see the PSO's ability to train the weight and refraction of FNN. The result of this research on 1000 hadith data show that model PSO-FNN with stemming process get 88.5% accuracy while without stemming process get 88.57% accuracy. Meanwhile, the result of comparative test between PSO-FNN with BP-FNN, the result shows that  PSO-FNN get accuracy equal to 88.57% which is lower 0.93% than BP-FNN which has 89.5% accuracy.
KLASIFIKASI AYAT AL-QURAN TERJEMAHAN BAHASA INGGRIS MENGGUNAKAN K-NEAREST NEIGHBOR (KNN) DAN INFORMATION GAIN Timami Hertza Putrisanni; Adiwijaya Adiwijaya; Said Al Faraby
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 3, No 1 (2019): Smart Device, Mobile Computing, and Big Data Analysis
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v3i1.1614

Abstract

Al-Quran is a holy book that contains instructions and instructions for the life of Muslims. In the Al-Quran there are interpretations quoted from the previous verse and have an implied meaning, so to be able to obtain these verses textually and contextually it is necessary to classify the interpretation of the Al-Quran to facilitate Muslims in finding topics in theAl-Quran. In this study, it is proposed to classify the topic of Al-Quran verses in English translation which consists of three classifications, namely commands, prohibitions and others. In this research the system design is done by collecting datasets, preprocessing to get clean data, selecting features using gain information, classifying using the K-Nearest Neighbor (KNN) method, and testing the system. The results of the tests conducted resulted in a value 64,10% for accuracy, 63% for precision, and 62.68% for recall using the value of k = 17 and the dataset containing data testing and data training of 1:9, respectively.Keywords: classification, topics of Al-Quran, K-Nearest Neighbor, Information gain.
Analisis dan Implementasi Metode Gabor Filter dan Support Vector Machine pada Klasifikasi Sidik Jari Intan Raharni Wijaya; Untari Novia Wisesty; Said Al Faraby
Indonesia Journal on Computing (Indo-JC) Vol. 2 No. 2 (2017): September, 2017
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2017.2.2.176

Abstract

Pengolahan citra digital semakin diminati, salah satunya pada sistem biometrik. Sistem biometrik merupakan sistem dalam pengenalan berdasarkan pola atau ciri khusus yang dimiliki makhluk hidup terutama manusia. Jenis identifikasi biometrik yang umum digunakan adalah pengenalan sidik jari. Sidik jari banyak digunakan dalam kehidupan sehari-hari selama lebih dari 100 tahun karena penerimaan yang tinggi, permanen, akurat, dan keunikan. Kelebihan sidik jari tersebut disebabkan oleh minutiae yang merupakan garis atau guratan pada sidik jari yang berbeda-beda setiap individu. Klasifikasi sidik jari secara umum terbagi menjadi dua tahap yakni ekstraksi fitur serta klasifikasi fitur.   Ektraksi fitur dapat dilakukan dengan cara filter seperti gabor filter dengan empat sudut orientasi yang berkisar 0, 45, 90 dan 135 derajat. Hasil dari ekstraksi ciri akan klasifikasi dengan tujutan identifikasi. Metode Support Vector Machine (SVM) dapat digunakan sebagai classifier untuk sistem biometrik sidik jari. SVM memiliki kernel trick yang berpengaruh pada akurasi yang dihasilkan. Digunakan SVM multiclass metode one-against-all dalam klasfikasi sidik jari untuk 25 kelas. Akurasi terbesar diperoleh oleh kernel Radial Basis Function (RBF) sebesar 73% untuk data awal dan 76% untuk penambahan data augmentasi