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All Journal Avatara Prosiding Semnastek Semesta Teknika Jurnal Studi Guru dan Pembelajaran Aksara: Jurnal Ilmu Pendidikan Nonformal JIKA (Jurnal Informatika) Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jutech: Jurnal Teknologi Informasi Jurnal Sistem Informasi Journal of Computer Science Advancements
YUSUF, DIANA
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Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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KIPRAH SUNDARI SOEKOTJO DALAM KANCAH MUSIK KERONCONG DI INDONESIA TAHUN 1977-2014 YUSUF, DIANA
Avatara Vol 4, No 2 (2016): Vol 4 Nomer 2 (Juli 2016)
Publisher : Jur. Pendidikan Sejarah FIS UNESA

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

Abstract

Sundari Soekotjo salah satu seniman musik keroncong yang namanya dikenal luas di Indonesia. Sundari Soekotjo mengawali karirnya dengan menjadi penyanyi pop cilik pada tahun 1977 Sundari Soekotjo mengkhususkan untuk mempelajari musik keroncong.selain seorang seniman keroncong Sundari Soekotjo merupakan seorang Doctor dan dosen di Universitas Negeri Jakarta dan Institut Bisnis Nusantara.Berdasarkan latar belakang tersebut, maka rumusan masalah adalah : (1) Bagaimana sejarah masuknya musik keroncong di Indonesia? (2) Apa latar belakang Sundari Soekotjo menekuni musik keroncong? (3) Bagaimana peran Sundari dalam mengembangkan musik keroncong di Indonesia 1977-2014? Metode penelitian yang digunakan adalah metode penelitian sejarah yang meliputi (1) Heuristik, pengumpulan data berupa artikel majalah, koran, buku penunjang, jurnal dan wawancara yang berkaitan dengan Sundari Soekotjo; (2) Kritik terhadap beberapa sumber primer dan sekunder yang sudah terkumpul; (3) Interpretasi data tentang peranan Sundari Soekotjo dalam perkembangan musik di Indonesia dengan hasil penelusuran sumber yang telah diperoleh; dan (4) Historiografi sesuai dengan tema yang dipilih yaitu peranan Sundari Soekotjo dalam perkembangan musik keroncong di Indonesia tahun 1977-2014.Hasil penelitian tentang Peranan Sundari Soekotjo dalam perkembangan musik keroncong dimulai sejak tahun 1977. Latar belakang Sundari Soekotjo menekuni musik keroncong adalah (1) Dukungan dari sang Ayah Soekotjo Ronodihardjo dan sang Ibu Hertini; (2) Mengikuti festival musik keroncong; dan (3) Sundari Soekotjo masuk ke dalam Sanggar Anggrek. Upaya yang dilakukan Sundari Soekotjo dalam melestarikan Musik Keroncong adalah (1) Sundari Soekotjo masuk dalam organisasi HAMKRI; (2) Memadukan musik keroncong dengan genre musik lainnya; dan (3) Mendirikan Yakin (Yayasan Musik Keroncong Indonesia). Kata Kunci: Musik Keroncong, Sundari Soekotjo, Peran.
Peran Orangtua Dalam Memahami Pendidikan Inklusi di TK Negeri Pembina Batumandi Fitriani, Fidha; Kurniati, Nia; Yusuf, Diana; Mildasari, Mildasari
Aksara: Jurnal Ilmu Pendidikan Nonformal Vol 10, No 1 (2024): January 2024
Publisher : Magister Pendidikan Nonformal Pascasarjana Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/aksara.10.1.417-425.2024

Abstract

Fokus utama dari penelitian ini adalah membahas peran orangtua dalam memahami tentang pendidikan inklusi di TK Negeri Pembina Batumandi dalam konteks pendidikan inklusi. Penelitian ini menggunakan pendekatan kualitatif dengan wawancara mendalam dan observasi partisipatif terhadap orang tua anak usia dini yang terlibat dalam program inklusi. Hasil penelitian menunjukkan bahwa peran orangtua dalam pendidikan inklusi dipengaruhi oleh faktor-faktor seperti pengetahuan, pengalaman pribadi, dan persepsi masyarakat. Selain itu, kolaborasi antara lembaga pendidikan dan orang tua dianggap krusial dalam menciptakan lingkungan pendidikan yang mendukung anak-anak dengan kebutuhan khusus. Penelitian ini memberikan rekomendasi untuk pengembangan program sosialisasi bagi orang tua dan peningkatan akses informasi terkait inklusi. Dengan membangun kesadaran orang tua, dapat menjadi kunci dalam mewujudkan pendidikan inklusi yang inklusif dan berkelanjutan pada anak usia dini.
Aplikasi Sistem Pakar Dengan Metode Forward Chaining Dan Certainty Factor Untuk Mendeteksi Penyakit Ayam Terisia, Vany; Yusuf, Diana
Jurnal Sistem Informasi (JUSIN) Vol 1 No 1 (2020): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v1i1.853

Abstract

The development of computer technology now is really fast. The computer is not only used as a tool to complete the work of humans but can also run applications designed to access information quickly. Application of expert system application is moved to a computer expert knowledge. So the computer could also resolve the problem as usual is done by specialists. Expert system has been widely developed in various fields, including the field of animal husbandry. Chicken breeders usually get the chicken disease information from the livestock extension officers. But sometimes often constrained because the number of extension officers of the cattle that are not evenly distributed in each area. Therefore, the breeder can access information about diseases of chicken with the help of the method of Forward Chaining and Certainty Factor into the making of the application. The resulting application is an expert system for detecting diseases of chickens that could be used to assist farmers in obtaining information about the disease of chickens and handling solutions.
Metode Decision Tree Dalam Klasifikasi Kredit Pada Nasabah PT Bank Perkreditan Rakyat (Studi Kasus : PT BPR Lubuk Raya Mandiri) Yusuf, Diana; Sestri, Ellya
Jurnal Sistem Informasi (JUSIN) Vol 1 No 1 (2020): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v1i1.855

Abstract

Data mining is a new technology that has been successfully applied in many fields. Many problems are solved by data mining algorithms C4.5 as a supporter.Classification is one of the methods contained in data mining and not a few researchers who use the classification methods in solving problems. Credit analysis will be done with the digging data against existing data customer credits based on atribut-atributnya with tekanik data mining algorithm C4.5. The algorithm C4.5 is itself a group of decision tree algorithm. This algorithm has input in the form of training and samples. Data mining technique used to classify loans with algorithm C4.5. Analysis and processing of data use applied tools of RapidMiner v 7.3
Implementasi Sistem Pakar Menggunakan Metode Forward Chaining Untuk Mendiagnosa Penyakit Rahim Yusuf, Diana; Terisia, Vany
Jurnal Sistem Informasi (JUSIN) Vol 2 No 2 (2021): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v2i2.1497

Abstract

The problem at the present time, especially in the field of health is part obstetrics uterine disease that causes high mortality rates in women , not only in the country of Indonesia is also the world . This happens due to the high number of deaths of women who develop uterine disease . Lack of information and knowledge on diseases of the uterus also cause new problems to the high rate of mortality. The public does not know the symptoms and attack patterns of uterine disease is due to a lack of information about the disease of the uterus. Application of expert system is a computer -based expert system that uses facts and reasoning techniques to solve problems that typically can only be solved by an expert or a doctor . The application of expert systems produce the output of an application program that can be used to diagnose diseases of the uterus in women based on facts or symptoms experienced by users. The method used is forward chaining. Testing the system with forward chaining method begins with questions about the facts or the symptoms experienced user, then the resulting conclusions based on the results of the analysis system .
Sistem Pakar Pengendalian Pemberian Kredit Kendaraan Bermotor Menggunakan Metode Forward Chaining Terisia, Vany; Yusuf, Diana; Arman, Shevty Arbekti
Jurnal Sistem Informasi (JUSIN) Vol 2 No 2 (2021): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v2i2.1498

Abstract

Artificial intelligence is the goal of computer utilization. Artificial intelligence is often referred to as artificial intelligence. Artificial intelligence is a branch of computer science. Artificial intelligence can help humans make decisions, which can find more accurate information in other words making computers easier to use with natural displays to make it easy to understand. Basically, the credit application system aims to provide the right convenience and service according to customer needs, and also provides a guideline and conditions in the credit application clearly so that customers understand how the procedure in applying for credit. The risks faced by lenders are PT. Kb such as credit arrears or bad credit. Therefore, it takes the accuracy of the internal finance party in conducting analysis. In this case it can be used to study the Expert System with the Forward Chaining method and find solutions to the problems faced to find out whether the actions to be taken by internal parties in processing the provision of motor vehicle loans in accordance with its rules and provisions
Penerapan Data Mining Untuk Memprediksi Pembelian Mobil Bekas Menggunakan Algoritma Naive Bayes Yusuf, Diana
Jurnal Sistem Informasi (JUSIN) Vol 3 No 1 (2022): Jurnal Sistem Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jusin.v3i1.2054

Abstract

Database can also be interpreted as a data warehouse. The amount of data collected in the database can be processed to generate valuable knowledge for science. One popular and widely used technique for processing databases is data mining. Data mining is the process of extracting knowledge from large and complex data warehouse. Data mining encompasses various algorithm to generate knowledge, one of which is naïve bayes. The dataset used in this research, employing the naïve bayes algorithm, consists of attributes relevant to the purchase of used cars, such year, transmission, mileage, car condition, and brand. This research aims to produce patterns and additional knowledge for participants in the used car business to identify the supporting factors in purchasing used cars.
Decision Tree Menggunakan Algoritma C4.5 Untuk Analisa Kelayakan Pemberian Kredit Yusuf, Diana; Bahri, Saeful; Larasati, Anggita
Jurnal Teknologi Informasi (JUTECH) Vol 2 No 2 (2021): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v2i2.1660

Abstract

Data mining is a new technology that has been successfully applied in many fields. Many problems are solved by data mining algorithms as a supporter. Classification is one of the methods contained in data mining and not a few researchers who use the classification methods in solving problems. Credit analysis will be done with the digging data against existing data customer credits based on atribute with data mining algorithm. The algorithm C4.5 is itself a group of decision tree algorithm. This algorithm has input in the form of training and samples. Data mining technique used to classify loans with algorithm C4.5. Analysis and processing of data use applied tools of RapidMiner.
Penerapan Model Infrastruktur Artificial Intelligence Sebagai Penggerak Industri 4.0 Syamsu, Muhajir; Terisia, Vany; Yusuf, Diana
Jurnal Teknologi Informasi (JUTECH) Vol 3 No 1 (2022): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v3i1.2375

Abstract

Artificial Intelligence (AI) memainkan peran yang sangat penting dalam mendorong Industri 4.0. yang mengacu pada transformasi industri yang didorong oleh teknologi digital, termasuk AI, Internet of Things (IoT), data besar, robotika, dan komputasi awan. AI memberikan kemampuan komputasi dan otomatisasi cerdas yang dapat meningkatkan efisiensi, produktivitas, dan inovasi di berbagai sektor industri, dengan berfokus pada pengembangan sistem komputer yang mampu melakukan tugas-tugas yang membutuhkan kecerdasan manusia, di mana AI melibatkan pembuatan mesin yang dapat belajar, merencanakan, beradaptasi, dan melakukan tugas-tugas cerdas seperti pengambilan keputusan, pengenalan suara atau gambar, pemrosesan bahasa alami, dan pemecahan masalah. Penelitian ini bertujuan untuk menyelidiki penerapan Model Infrastruktur Artificial Intelligence (AI) sebagai pendorong Industri 4.0. Penelitian ini akan melibatkan pengembangan model AI yang dapat digunakan dalam konteks industri modern untuk meningkatkan efisiensi, produktivitas, dan inovasi. Dengan menggunakan metode analisis kebutuhan, metode ini melibatkan analisis mendalam terhadap target industri, baik dari segi infrastruktur maupun kebutuhan yang harus dipenuhi. Melalui wawancara, observasi, dan analisis data, peneliti mampu mengidentifikasi area-area di mana teknologi AI dapat memberikan dampak dan solusi tepat guna yang mampu memberikan manfaat signifikan dalam meningkatkan efisiensi, produktivitas, kualitas, dan inovasi di industri, dengan implementasi yang kuat terhadap kebutuhan industri, pengumpulan dan pengolahan data yang baik, adaptasi dengan konteks dan peraturan yang berlaku untuk Industri 4.0 di perusahaan.
Classification of Brain Image Tumor using EfficientNet B1-B2 Deep Learning Hastomo, Widi; Karno, Adhitio Satyo Bayangkari; Sestri, Ellya; Terisia, Vany; Yusuf, Diana; Arman, Shevty Arbekti; Arif, Dodi
Semesta Teknika Vol 27, No 1 (2024): MEI
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.v27i1.19691

Abstract

In this study, a new neural network model (EfficientNet B1-B2) was sought for the detection of brain tumors in magnetic resonance imaging (MRI) images. The primary objective was to achieve high accuracy rates so as to classify the images. The deep learning techniques meticulously processed and increased the data augmentation as much as possible for the EfficientNet B1-B2 models. Our experimental results show an accuracy of 98% in the B1 version in Table II. This provides a potentially optimistic view of the application of artificial intelligence technology to disease diagnosis based on medical image analysis. Nonetheless, we must remind ourselves that the dataset we used has limitations in terms of the challenges it can pose. Although the number of potential variations of actual medical images constitutes a major challenge, it is not the only one. Most medical datasets are unbalanced, contain highly variable noise, have a slow internal structure, and are often small in size. Hence, our end goal is to help stimulate not only the field of brain tumor detection and treatment but also the development of more sophisticated classification models in the health context.
Co-Authors Dodi Arif Fahrul Razi Fitriani, Fidha Guilin, Xie IRMA YULIANTINA, IRMA Jiao, Deng Karno, Adhitio Satyo Bayangkari Larasati, Anggita Listianto, Farros Fauzaan Mildasari, Mildasari Nia Kurniati Nuri Aslami Nurjayanti , Revi Risaldi, Fahmi Sestri, Elliya Sestri, Ellya Shevti Arbekti Arman Syamsu, Muhajir Vany Terisia Widi Hastomo