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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer dan Informasi Jurnal Ilmu Komputer Jurnal Teknik ITS Majalah Kedokteran Bandung IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Kursor Jurnal technoscientia Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications REKAYASA JPM17: Jurnal Pengabdian Masyarakat POROS TEKNIK Annual Research Seminar Register: Jurnal Ilmiah Teknologi Sistem Informasi EMITTER International Journal of Engineering Technology Jurnal Inspiration Dental Journal (Majalah Kedokteran Gigi) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal ULTIMATICS Journal of Computer Science and Informatics Engineering (J-Cosine) Systemic: Information System and Informatics Journal Specta Journal of Technology EPI International Journal of Engineering ILKOMNIKA: Journal of Computer Science and Applied Informatics Indonesian Journal of Electrical Engineering and Computer Science JURNAL TEKNOLOGI TECHNOSCIENTIA Makara Journal of Technology Sewagati Nusantara Journal of Computers and its Applications Jurnal INFOTEL
Agus Zainal Arifin
Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember Surabaya
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Indonesian News Classification Using Naïve Bayes and Two-Phase Feature Selection Model M. Ali Fauzi; Agus Zainal Arifin; Sonny Christiano Gosaria
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 3: December 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i3.pp610-615

Abstract

Since the rise of WWW, information available online is growing rapidly. One of the example is Indonesian online news. Therefore, automatic text classification became very important task for information filtering. One of the major issue in text classification is its high dimensionality of feature space. Most of the features are irrelevant, noisy, and redundant, which may decline the accuracy of the system. Hence, feature selection is needed. Maximal Marginal Relevance for Feature Selection (MMR-FS) has been proven to be a good feature selection for text with many redundant features, but it has high computational complexity. In this paper, we propose a two-phased feature selection method. In the first phase, to lower the complexity of MMR-FS we utilize Information Gain first to reduce features. This reduced feature will be selected using MMR-FS in the second phase. The experiment result showed that our new method can reach the best accuracy by 86%. This new method could lower the complexity of MMR-FS but still retain its accuracy.
Knowledge Dictionary for Information Extraction on the Arabic Text Data Saputra, Wahyu Syaifullah Jauharis; Arifin, Agus Zainal; Yuniarti, Anny
Makara Journal of Technology Vol. 16, No. 2
Publisher : UI Scholars Hub

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

Abstract

Information extraction is an early stage of a process of textual data analysis. Information extraction is required to get information from textual data that can be used for process analysis, such as classification and categorization. A textual data is strongly influenced by the language. Arabic is gaining a significant attention in many studies because Arabic language is very different from others, and in contrast to other languages, tools and research on the Arabic language is still lacking. The information extracted using the knowledge dictionary is a concept of expression. A knowledge dictionary is usually constructed manually by an expert and this would take a long time and is specific to a problem only. This paper proposed a method for automatically building a knowledge dictionary. Dictionary knowledge is formed by classifying sentences having the same concept, assuming that they will have a high similarity value. The concept that has been extracted can be used as features for subsequent computational process such as classification or categorization. Dataset used in this paper was the Arabic text dataset. Extraction result was tested by using a decision tree classification engine and the highest precision value obtained was 71.0% while the highest recall value was 75.0%.
Pengukuran Kemiripan berbasis Leksikal dan Semantik untuk Perangkingan Dokumen Berbahasa Arab Syadza Anggraini; Diana Purwitasari; Agus Zainal Arifin
ILKOMNIKA: Journal of Computer Science and Applied Informatics Vol 4 No 2 (2022): Volume 4, Nomor 2, Agustus 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v4i2.495

Abstract

Hasil pencarian relevan pada sistem temu kembali informasi tergantung pengukuran kemiripan antara query dan dokumen berdasarkan bobot kata query terhadap dokumen yang akan dirangking. Namun, perhitungan kemiripan menggunakan bobot kata dimungkinkan adanya lafal kata yang berbeda tetapi memiliki makna sama. Hasil dokumen pencarian teks berbahasa Arab akan dipengaruhi kemampuan pengguna yang beragam dalam memahami bahasa tersebut. Oleh karena itu diusulkan pengukuran kemiripan secara leksikal untuk mengatasi lafal kata yang beda serta juga menggunakan kemiripan secara semantik untuk mengenali kata dengan makna sama. Penggabungan perhitungan kemiripan leksikal dan semantik dilakukan berdasarkan bobot kata (secara leksikal) yang digabungkan dengan word embedding (secara semantik). Hasil dari uji coba dilakukan pada 2900 kitab berbahasa Arab Maktabah Syamilah menunjukkan keunggulan dengan rata-rata f-measure tertinggi dibandingkan metode lainnya yaitu 66.7% pada keseluruhan query, serta 65.2% dan 69% pada short query dan long query. Short query adalah frekuensi jumlah kata di dalam query yang berjumlah 1-2 kata sedangkan long query adalah frekuensi jumlah kata di dalam query yang berjumlah lebih dari 2 kata. Short query dan long query berpeluang me-retrieve dokumen yang tidak relevan. Hasil retrieve dokumen yang tidak relevan disebabkan karena rendahnya kemiripan antar kata di dalam suatu query akibat pemilihan kata yang kurang tepat. Pemilihan kata-kata query membutuhkan penguasaan pengguna yang tidak hanya mampu mengolah query dalam bahasa Arab, tetapi juga dapat memahami konteks dokumen yang akan dicari.
Feature Selection Using Hybrid Binary Grey Wolf Optimizer for Arabic Text Classification Muhammad Bahrul Subkhi; Chastine Fatichah; Agus Zainal Arifin
IPTEK The Journal for Technology and Science Vol 33, No 2 (2022)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v33i2.13769

Abstract

Feature selection in Arabic text is a challenging task due to the complex and rich nature of Arabic. The feature selection requires solution quality, stability, conver- gence speed, and the ability to find the global optimal. This study proposes a feature selection method using the Hybrid Binary Gray Wolf Optimizer (HBGWO) for Ara- bic text classification. The HBGWO method combines the local search capabilities or exploratory of the BGWO and the search capabilities around the best solutions or exploits of the PSO. HBGWO method also combines SCA’s capabilities in finding global solutions. The data set used Arabic text from islambook.com, which consists of five Hadith books. The books selected five classes: Tauhid, Prayer, Zakat, Fasting, and Hajj. The results showed that the BGWO-PSO-SCA feature selection method with the fitness function search and classification method using SVM could per- form better on Arabic text classification problems. BGWO-PSO with fitness function and the classification method using SVM (C=1.0) gives a high accuracy value of 76.37% compared to without feature selection. The BGWO-PSO-SCA feature selec- tion method provides an accuracy value of 88.08%. This accuracy value is higher than the BGWO-PSO feature selection and other feature selection methods.
Pemanfaatan E-commerce dan Media Sosial Guna Meningkatkan Ekonomi dan Proses Bisnis UMKM Koppontren NURILA Bangkalan Dini Adni Navastara; Nanik Suciati; Chastine Fatichah; Handayani Tjandrasa; Agus Zainal Arifin; Zakiya Azizah Cahyaningtyas; Yulia Niza; Evelyn Sierra; Daniel Sugianto; Kevin Christian Hadinata; Salim Bin Usman; Muhammad Fikri Sunandar; Fiqey Indriati Eka Sari
Sewagati Vol 6 No 4 (2022)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (861.366 KB) | DOI: 10.12962/j26139960.v6i4.135

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) memiliki peran yang besar dalam bidang industri dan ekonomi suatu negara. Di era digital ini, pemanfaatan teknologi untuk meningkatkan produktifitas UMKM sudah marak dilakukan. Sayangnya pemanfaatan tekonologi ini belum diterapkan pada UMKM dari Koperasi Pondok Pesantren Addimyathy Nurul Iman Labang (Koppontren NURILA). Tim pengabdi berinisiatif melaksanakan pelatihan untuk meningkatkan produktifitas UMKM Koppontren NURILA. Kegiatan terbagi menjadi empat tahap yaitu persiapan, pelatihan, pendampingan, dan evaluasi. Kegiatan ini mengangkat topik tentang pemanfaatan e-commerce dan media sosial untuk peningkatan ekonomi dan proses bisnis UMKM. Pelaksanaan pelatihan dan pendampingan dilakukan secara hybrid, yaitu daring dan luring di lokasi UMKM Koppontren NURILA. Berdasarkan hasil evaluasi, peserta kegiatan merasa puas terhadap kualitas materi dengan nilai 4.35 dari skala 5.
Pemanfaatan Platform Google Classroom untuk Pembelajaran Daring di Pondok Pesantren Miftahul Ulum Al-Islamy, Bangkalan, Madura Dini Adni Navastara; Nanik Suciati; Chastine Fatichah; Diana Purwitasari; Handayani Tjandrasa; Agus Zainal Arifin; Akwila Feliciano; Yulia Niza; Rangga Kusuma Dinata; Safhira Maharani; Ahmad Syauqi; Sherly Rosa Anggraeni; Fandy Kuncoro Adianto; Zakiya Azizah Cahyaningtyas; Salim Bin Usman; Kevin Christian Hadinata
Sewagati Vol 4 No 3 (2020)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (269.198 KB)

Abstract

Proses pembelajaran daring menjadi hambatan tersendiri dalam bidang pendidikan, terlebih untuk pendidikan wajib yang harus dilakukan secara bertatap muka langsung antara pengajar dan pelajar. Di luar faktor permasalahan eksternal, permasalahan internal perlu diselesaikan terlebih dahulu, yaitu media pembelajaran. Salah satu platform digital yang tersedia sebagai media pembelajaran untuk menunjang pembelajaran secara daring adalah Google Classroom. Aplikasi Google Classroom berbasis web yang berbentuk pembelajaran asynchronous atau dapat dikatakan pemberian materi ajar dilakukan secara tidak langsung. Walaupun sebuah media daring sudah tersedia, masih ada yang belum mengenal atau memahami penggunaan aplikasi Google Classroom sebagai media ajar mereka. Oleh karena itu, kami mengadakan pengabdian masyarakat berupa pelatihan tentang penggunaan aplikasi Google Classroom bagi guru-guru di Pondok Pesantren Miftahul Ulum Al-Islamy, yang berada di Bangkalan, Madura. Selain itu, tim pengabdi juga melakukan pendampingan bagi guru-guru dalam mempraktikkan penggunaan Google Classroom sesuai dengan mata pelajaran yang diajar. Berdasarkan hasil survei, sebanyak 91% dari total peserta pelatihan menyebutkan bahwa pelatihan ini dapat meningkatkan pengetahuan dan kemampuan secara softskill dan hardskill para guru.
Deteksi Bot Spammer Twitter Berbasis Time Interval Entropy dan Global Vectors for Word Representations Tweet’s Hashtag Arif Mudi Priyatno; Muhammad Mirza Muttaqi; Fahmi Syuhada; Agus Zainal Arifin
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol. 5 No. 1 (2019): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v5i1.1382

Abstract

Bot spammer merupakan penyalahgunaan user dalam menggunakan Twitter untuk menyebarkan pesan spam sesuai dengan keinginan user. Tujuan spam mencapai trending topik yang ingin dibuatnya. Penelitian ini mengusulkan deteksi bot spammer pada Twitter berbasis Time Interval Entropy dan global vectors for word representations (Glove). Time Interval Entropy digunakan untuk mengklasifikasi akun bot berdasarkan deret waktu pembuatan tweet. Glove digunakan untuk melihat co-occurrence kata tweet yang disertai Hashtag untuk proses klasifikasi menggunakan Convolutional Neural Network (CNN). Penelitian ini menggunakan data API Twitter dari 18 akun bot dan 14 akun legitimasi dengan 1.000 tweet per akunnya. Hasil terbaik recall, precision, dan f-measure yang didapatkan yaitu 100%; 100%, dan 100%. Hal ini membuktikan bahwa Glove dan Time Interval Entropy sukses mendeteksi bot spammer dengan sangat baik. Hashtag memiliki pengaruh untuk meningkatkan deteksi bot spammer.  Spam spammers are users' misuse of using Twitter to spread spam messages in accordance with user wishes. The purpose of spam is to reach the required trending topic. This study proposes detection of bot spammers on Twitter based on Time Interval Entropy and global vectors for word representations (Glove). Time Interval Entropy is used to classify bot accounts based on the tweet's time series, while glove views the co-occurrence of tweet words with Hashtags for classification processes using the Convolutional Neural Network (CNN). This study uses Twitter API data from 18 bot accounts and 14 legitimacy accounts with 1000 tweets per account. The best results of recall, precision, and f-measure were 100%respectively. This proves that Glove and Time Interval Entropy successfully detects spams, with Hash tags able to increase the detection of bot spammers.
STRATEGI PEMILIHAN KALIMAT PADA PERINGKASAN MULTI DOKUMEN Satrio Verdianto; Agus Zainal Arifin; Diana Purwitasari
NJCA (Nusantara Journal of Computers and Its Applications) Vol 1, No 2 (2016): Desember 2016
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v1i2.14

Abstract

Ringkasan berita diartikan sebagai teks yang dihasilkan dari satu atau lebih kalimat yang menyampaikan informasi penting dari berita. Salah satu fase penting dalam peringkasan adalah pembobotan kalimat (sentence scoring). Dimana pada peringkasan berita, metode pembobotannya sebagian besar menggunakan fitur dari berita sendiri. Berdasarkan hasil dari penelitian [3] bahwa untuk pembobotan kalimat pada dokumen yang memiliki karakter teks pendek dan terstruktur seperti berita maka teknik pembobotan kalimat terbaik adalah dengan menggunakan kombinasi dari keempat fitur yaitu word frequency, TF-IDF, posisi kalimat, dan kemiripan kalimat terhadap judul (Resemblance to the title ). Pada penelitian ini kombinasi keempat fitur tersebut dibandingkan dengan kombinasi tiga fitur dan dua fitur dan dievaluasi menggunakan nilai ROUGE-N dan dievaluasi berdasarkan lama waktu eksekusi. Berdasarkan hasil uji coba didapatkan hasil bahwa yang paling optimal diantara keempat kombinasi fitur tersebut adalah kombinasi antara dua buah fitur yakni fitur posisi kalimat dan word frequency dengan nilai ROUGE-N sebesar 0.679 dan lama waktu eksekusi 28.458 detik.
ANALYSIS OF ADAPTIF LOCAL REGION IMPLEMENTATION ON LOCAL THRESHOLDING METHOD I Gusti Agung Socrates Adi Guna; Hendra Maulana; Agus Zainal Arifin; Dini Adni Navastara
NJCA (Nusantara Journal of Computers and Its Applications) Vol 1, No 2 (2016): Desember 2016
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v1i2.10

Abstract

Thresholding is a simple and effective technique for image segmentation. Thresholding techniques can begrouped into two categories, global thresholding and local thresholding. All local threshold method generallybegins with determining thresholds in each pixel by checking the area centered on the pixel, using a box shape (x,y) which is fixed by the size of the neighborhood "b". If the neighborhood is very small, then the algorithm will besensitive to noise and excessive segmentation occurs. Whereas, if the size of the neighborhood is very large thenthe algorithm will apply resemble the global threshold method. In this study, we propose a method of calculationof Local Adaptive Region, to determine the value of each pixel that is flexible neighborhoods, where each pixelhas values different neighborhoods based on the value of the standard deviation region. Adaptive method on thelocal region thresholding consists of several processes, namely: Image Enhancement, Adaptive Local Region andthresholding. Based on evaluation of ME, image result of threshold using the Adaptive Local Region method, givingan average ME smallest value, that is 16.99% at Niblack method and 19.46% at Sauvola method. And onevaluation of the RAE, image result of threshold using the Adaptive Local Region method, giving an averageRAE smallest value, that is 15.26% at Niblack method and 25.58% at Sauvola method. In addition, the results oftrials with various noise variance represent that the method of Adaptive Local Region resistant to noise.
IMAGE THRESHOLDING BASED ON HIERARCHICAL CLUSTERING ANALYSIS AND PERCENTILE METHOD FOR TUNA IMAGE SEGMENTATION Alifia Puspaningrum; Nahya Nur; Ozzy Secio Riza; Agus Zainal Arifin
NJCA (Nusantara Journal of Computers and Its Applications) Vol 2, No 1 (2017): Juni 2017
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v2i1.24

Abstract

Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.
Co-Authors - Azhari AA Sudharmawan, AA Adenuar Purnomo Adhi Nurilham Adi Guna, I Gusti Agung Socrates Afrizal Laksita Akbar Ahmad Afiif Naufal Ahmad Reza Musthafa, Ahmad Reza Ahmad Syauqi Aida Muflichah Aidila Fitri Fitri Heddyanna Akira Asano Akira Taguchi Akwila Feliciano Alhaji Sheku Sankoh, Alhaji Sheku Alif Akbar Fitrawan, Alif Akbar Alifia Puspaningrum Alqis Rausanfita Amelia Devi Putri Ariyanto Aminul Wahib Aminul Wahib Aminul Wahib Ana Tsalitsatun Ni'mah Andi Baso Kaswar Andi Baso Kaswar Anindhita Sigit Nugroho Anindita Sigit Nugroho Anny Yunairti Anny Yuniarti Anto Satriyo Nugroho Arif Fadllullah Arif Mudi Priyatno Arifin, M. Jainal Arifin, M. Jainal Arifzan Razak Arini Rosyadi Arrie Kurniawardhani Arya Widyadhana Arya Yudhi Wijaya Bagus Satria Wiguna Bagus Setya Rintyarna Baskoro Nugroho Bilqis Amaliah Chandranegara, Didih Rizki Chastine Fatichah Christian Sri kusuma Aditya, Christian Sri kusuma Cinthia Vairra Hudiyanti Cornelius Bagus Purnama Putra Daniel Sugianto Daniel Swanjaya Darlis Herumurti Dasrit Debora Kamudi Desepta Isna Ulumi Desmin Tuwohingide Dhian Kartika Diana Purwitasari Didih Rizki Chandranegara Dika Rizky Yunianto Dimas Fanny Hebrasianto Permadi Dini Adni Navastara, Dini Adni Dinial Utami Nurul Qomariah Dwi Ari Suryaningrum Dyah S. Rahayu Eha Renwi Astuti Endang Juliastuti Erliyah Nurul Jannah, Erliyah Nurul Ery Permana Yudha Eva Firdayanti Bisono Evan Tanuwijaya Evelyn Sierra Fahmi Syuhada Fahmi Syuhada Fandy Kuncoro Adianto Fathoni, Kholid Fathoni, Kholid Fiqey Indriati Eka Sari Gosario, Sony Gulpi Qorik Oktagalu Pratamasunu Gus Nanang Syaifuddiin Handayani Tjandrasa Hanif Affandi Hartanto Hudan Studiawan Humaira, Fitrah Maharani Humaira, Fitrah Maharani I Guna Adi Socrates I Gusti Agung Socrates Adi Guna I Made Widiartha I Putu Gede Hendra Suputra Indra Lukmana Irna Dwi Anggraeni Ismail Eko Prayitno Rozi Januar Adi Putra Kevin Christian Hadinata Khadijah F. Hayati Khairiyyah Nur Aisyah Khairiyyah Nur Aisyah, Khairiyyah Nur Khalid Khalid Khoirul Umam Lafnidita Farosanti Laili Cahyani Lutfiani Ratna Dewi Luthfi Atikah M. Ali Fauzi Mamluatul Hani’ah Maulana, Hendra Maulana, Hendra Mika Parwita Moch Zawaruddin Abdullah Moh. Zikky, Moh. Mohammad Fatoni Anggris, Mohammad Fatoni Mohammad Sonhaji Akbar Muhamad Nasir Muhammad Bahrul Subkhi Muhammad Fikri Sunandar Muhammad Imron Rosadi Muhammad Imron Rosadi Muhammad Machmud Muhammad Mirza Muttaqi Muhammad Muharrom Al Haromainy Munjiah Nur Saadah Muttaqi, Muhammad Mirza Nahya Nur Nanang Fakhrur Rozi Nanik Suciati Nina Kadaritna Novi Nur Putriwijaya Novrindah Alvi Hasanah Nur, Nahya Nuraisa Novia Hidayati Nursanti Novi Arisa Nursuci Putri Husain Ozzy Secio Riza Pangestu Widodo, Pangestu Pasnur Pasnur Pasnur Pasnur Puji Budi Setia Asih Putri Damayanti Putri Nur Rahayu Putu Praba Santika Rangga Kusuma Dinata Rarasmaya Indraswari Ratri Enggar Pawening Renest Danardono Resti Ludviani Rigga Widar Atmagi Riyanarto Sarno Riza, Ozzy Secio Rizka Sholikah Rizka Wakhidatus Sholikah Rizqa Raaiqa Bintana Rizqi Okta Ekoputris Rosyadi, Ahmad Wahyu Ryfial Azhar, Ryfial Safhira Maharani Safri Adam Saiful Bahri Musa Salim Bin Usman Saputra, Wahyu Syaifullah Jauharis Satrio Verdianto Satrio Verdianto Setyawan, Dimas Ari Sherly Rosa Anggraeni Siprianus Septian Manek Sonny Christiano Gosaria Sugiyanto, Sugiyanto Suprijanto Suprijanto Suwanto Afiadi Syadza Anggraini Syuhada, Fahmi Takashi Nakamoto Tegar Palyus Fiqar Tesa Eranti Putri Tio Darmawan Umi Salamah Undang Rosidin Verdianto, Satrio Waluya, Onny Kartika Wanvy Arifha Saputra Wardhana, Septiyawan R. Wawan Gunawan Wawan Gunawan Wawan Gunawan Wawan Gunawan Wijayanti Nurul Khotimah Yudhi Diputra Yufis Azhar Yulia Niza Yunianto, Dika R. Zainal Abidin Zakiya Azizah Cahyaningtyas