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All Journal Jurnal Teknologi Jurnal sistem informasi, Teknologi informasi dan komputer Prosiding Seminar Nasional Teknoka JURNAL PENDIDIKAN TAMBUSAI Jurnal Teknologi Terpadu Society: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Dedication : Jurnal Pengabdian Masyarakat
Nurvelly Rosanti, Nurvelly
Universitas Muhammadiyah Jakarta
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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PENGGUNAAN METODE JALUR KRITIS PADA MANAJEMEN PROYEK (STUDI KASUS: PT. TREND COMMUNICATIONS INTERNATIONAL) Rosanti, Nurvelly; Setiawan, Erwin; Ayuningtyas, Asti
Jurnal Teknologi Vol 8, No 1 (2016): Jurnal Teknologi
Publisher : Fakultas Teknik Universitas Muhammadiyah Jakarta

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Abstract

Manajemen proyek merupakan bagian yang penting dalam proyek pengembangan perangkat lunak, karenanya penggunaan teknologi untuk mendukung efektifitas dan efisiensi dari manajemen proyek. PT. Trend Communications International (TRENDcom) merupakan sebuah perusahaan penyedia jasa Project Management Office (PMO) dengan menawarkan keahlian mereka dalam mengelola proyek-proyek IT. Salah satu masalah yang mereka hadapi adalah dalam mengelola waktu. Keterlambatan proyek disebabkan oleh berbagai faktor yang berhubungan dengan manusia, proses dan teknologi. Dari aspek teknologi, tidak adanya aplikasi yang mampu menyediakan informasi bagi manajer proyek dan timnya tentang aktifitas yang berpotensi menyebabkan keterlambatan proyek secara keselurahan, serta tugas yang sering terlewatkan tenggat waktunya merupakan penyebabnya. Dengan menggunakan Metode Jalur Kritis, sebuah aplikasi telah dibangun untuk menyelesaikan permasalahan tersebut. Aplikasi tersebut dapat menunjukan informasi jalur kritis atau jalur yang aktifitasnya perlu dimonitor dengan seksama sehingga dapat diprioritaskan dan fitur pengingat untuk aktifitas yang tenggat waktunya sudah dekat.
Penggunaan Metode Jalur Kritis pada Manajemen Proyek (Studi Kasus: PT. Trend Communications International) Rosanti, Nurvelly; Setiawan, Erwin; Ayuningtyas, Asti
Prosiding Seminar Nasional Teknoka Vol 1 (2016): Prosiding Seminar Nasional Teknoka ke - 1
Publisher : Fakultas Teknik, Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta

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Abstract

Project Management has been an important part of software development projects, therefore the use of technology to endorse effectiveness and efficiency of Project Management is a crucial aspect. PT. Trend Communications Internation (TRENDcom) is a company providing Project Management Office (PMO) service. Their main service is to provide expertise in managing Information Technology projects. One of the big issues they are facing is managing timeline. Late of delivery is caused by people, process and technology reasons. Technology-wise, the absence of application to let project manager and his PMO team the important activities that will cause delay on overall progress as well as the lack of reminder for their due project activities. Using Critical Path Method, an application is developed to address the issues. The application managed to give information of activities in a schedule which has big impact on overall delivery timeline and to provide reminder of due activities.
ANALISIS TENDENSI PORTAL BERITA ONLINE TERHADAP VAKSINASI COVID-19 DI INDONESIA MENGGUNAKAN METODE K-NEAREST NEIGHBOR Adharani, Yana; Saputra, Ambar Dwi; Rosanti, Nurvelly; Latifah, Retnani
Jurnal Sistem Informasi, Teknologi Informatika dan Komputer Volume 13 No 2, Januari Tahun 2023
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/justit.13.2.125-137

Abstract

Di Indonesia per 27 Juli 2021 ada 4.170.155 kasus terkonfirmasi COVID-19. Salah satu cara untuk menekan peningkatan dan penyebaran kasus COVID-19 adalah dengan melakukan vaksinasi. Terkait dengan hal tersebut, ada beberapa berita tentang vaksin COVID-19 di portal berita. Pemberitaan yang diberikan memiliki tendensi positif, negatif maupun netral yang dapat memengaruhi pandangan masyarakat terhadap pemberian vaksin COVID-19. Dalam penelitian ini dilakukan klasifikasi terhadap tendensi pemberitaan portal berita terhadap vaksin COVID-19 menggunakan metode K-Nearest Neigbor (KNN). Pengujian dilakukan terhadap 1000 data dengan 5 kombinasi data training dan testing sebanyak 50 kali dengan nilai k berbeda. Pelabelan data dilakukan secara manual dan otomatis menggunakan sentistrenth_id. Hasil Pengujian pelabelan manual menunjukan tingkat accuracy tertinggi sebesar 78,50% pada k = 6. Untuk pelabelan otomatis diperoleh accuracy tertinggi sebesar 93% pada k = 6. Accuracy tertinggi diperoleh untuk penggunaan data training sebesar 80% dan data testing 20%. Tingkat accuracy yang belum optimal diakibatkan karena jumlah data pada setiap kelas tidak berimbang dan terdapat pelabelan pada data training maupun testing yang tidak akurat. Aplikasi dapat mengidentifikasi kata-kata dominan, baik dalam pemberitaan positif, negatif maupun netral untuk kelas yang teridentifikasi.Kata Kunci: klasifikasi, COVID-19, K-Nearest Neigbor, portal berita, vaksinasi
Penerapan Model Machine Learning Untuk Menentukan Klasifikasi Jenis Bantuan Sosial Rosanti, Nurvelly; Iqbal, Muhammad; Munir, Sirojul
Jurnal Teknologi Terpadu Vol 8 No 2 (2022): Desember, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i2.604

Abstract

The Provincial Government of DKI Jakarta has a social assistance program budgeted by the APBD in the form of the Jakarta Elderly Card (KLJ), Jakarta Persons with Disabilities Card (KPDJ) and Jakarta Child Card (KAJ) programs. The problems that occur at the Kelurahan level are related to social assistance, namely the difficulty in determining the right type of assistance to be received by residents according to the terms and criteria that have been determined by the Government and there is no overlapping of recipients of assistance. The registration factor and the lack of understanding of residents regarding the criteria for the type of social assistance resulted in the determination of recipients of social assistance not being on target, such as residents receiving assistance who did not meet the criteria, resulting in social jealousy. To help with this problem, research was carried out to determine the best model in classifying the types of social assistance based on recipient criteria by comparing three classification methods. This study uses 100 respondent data and 8 criteria used as determinants of recipients. Comparison of the Certainty Factor, Naïve Bayes and Decision Tree models will provide an overview of the best model based on the level of accuracy. The confusion matrix is used to test the accuracy for Naïve Bayes and Decision Tree and the output of the selected model is a web-based application that can provide recommendations for types of social assistance. The best accuracy results are Certainty Factor which is 98.4%, Naïve Bayes and Decision Tree is 93.3%.
Pengaruh Jarak Objek Citra pada Model Deteksi dan Klasifikasi Botol Plastik menggunakan YOLO Rosanti, Nurvelly; Latifah, Retnani; Munir, Sirojul; Maududi, Izzuddin Al Qossam
Jurnal Teknologi Terpadu Vol 10 No 1 (2024): Juli, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i1.1247

Abstract

Plastic bottle waste must be separated based on shape and size to facilitate recycling. Sorting plastic bottles can use object detection technology to facilitate classification using images. Image distance capture affects the classification of bottle waste because large bottles will look small when seen from a distance and vice versa. This study aims to create a plastic bottle detection and classification model using the YOLOv8 algorithm with the same bottle shape but different sizes and measure the effect of image distance on the model. Bottles consist of three sizes: large bottles measuring 1500 ml, medium bottles measuring 600 ml, and small sizes 330 ml. Pictures for the bottle image dataset were shot between 80 and 100 centimeters away. Robotoflow was used to produce the dataset. Model performance evaluation used Mean Average Precision, and model testing used a confusion matrix. The test results for the same model with an image capture distance had an accuracy value of 100%. Testing of 80 cm distance images applied to the 100 cm model had an accuracy of 67%. Testing for 100 cm distance images applied to the 80 cm model was still quite good, with an accuracy of 91.6%. The results obtained show that the image distance affects the results of the model that has been built, so use an image that matches the distance applied to the model.
PENGOLAHAN DATA: PEMAHAMAN GEMPA BUMI DI INDONESIA MELALUI PENDEKATAN DATA MINING Faridzi, Salman Al; Faza Shafa Azizah; Mustafa, Faizal; Nindya Putri, Azzahra; Ramadhika, Gilang; Rizky Aditya, Fauzan; Sherli Fadilah, Ridha; Habibi, Yusuf; Sutrisno, Mirza; Jumail, Jumail; Dewi Risanty, Rita; Rosanti, Nurvelly
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 2 No. 1 (2024): Februari
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v2i1.506

Abstract

Gempa bumi merupakan bencana alam yang sering terjadi di Indonesia akibat interaksi lempeng tektonik. Indonesia terletak pada pertemuan empat lempeng tektonik dunia, yang menyebabkan aktivitas zona tumbukan dan patahan yang berpotensi memicu gempa bumi. Meskipun telah terjadi sejumlah peristiwa gempa bumi besar di Indonesia, prediksi gempa secara tepat waktu masih sulit karena kompleksitas geologi dan dinamika kerak bumi. Peningkatan pemahaman tentang perilaku geologi dan sistem peringatan dini menjadi kunci dalam mempersiapkan diri menghadapi ancaman gempa bumi di masa mendatang. Data mining adalah proses yang berguna untuk mengeksplorasi dan mencari nilai informasi kompleks yang tersimpan dalam basis data. Dengan menggunakan data mining, dampak atau akibat dari gempa bumi yang terjadi di Indonesia dapat dipelajari berdasarkan data gempa bumi yang telah terjadi sebelumnya. Maka, dilakukanlah webinar dan workshop tentang penggunaan data mining untuk memahami pola gempa bumi di Indonesia selama 10 tahun terakhir. Webinar membahas dasar-dasar data mining dan fakta gempa yang terjadi di Indonesia, sementara workshop membahas pengolahan dan visualisasi data gempa bumi menggunakan bahasa Python dan Google Colab. Workshop ini terbatas pada pengolahan dan visualisasi data csv gempa bumi saja. Kegiatan webinar dan workshop dilaksanakan pada tanggal 29 Januari 2024 pukul 13.00 WIB. Hasil evaluasi menunjukkan bahwa peserta menyatakan kepuasan mereka terhadap acara tersebut, dengan sebagian besar peserta memberikan nilai positif terhadap penyampaian materi, kesesuaian materi dengan tema, kejelasan informasi, serta kualitas audio visual selama acara berlangsung.
TECH TALK : SOFTWARE DEVELOPMENT TRAINING AND FRAMEWORK UTILIZATION FOR WEBSITE DEVELOPMENT Maulana, Mirza; Abyan Shidqi, Muhammad; Alifsyah Ramadhan, Rafi Novranza; Arista Putri, Syahda; Fadhlurokhman, Rafi; Haykal Andana, Muhammad; Cholis Arethusa, Muhammad; Khoirrosyid, Haida; Guritno, Deden; Mujiastuti, Rully; Rosanti, Nurvelly; Nurbaya Ambo, Sitti
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 2 No. 1 (2024): Februari
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v2i1.508

Abstract

Program Magang dan Studi Independen Bersertifikat (MSIB) merupakan inisiatif dari Kementrian Pendidikan, Kebudayaan, Riset, dan Teknologi (Kemdikbudristek) untuk memberikan kesempatan kepada mahasiswa mengembangkan keterampilan di luar perkuliahan, dengan fokus pada pengembangan perangkat lunak. Kegiatan ini bertujuan memberikan pemahaman dan keterampilan dasar dalam pengembangan perangkat lunak kepada mahasiswa dan masyarakat umum. Metode pelaksanaan kegiatan terdiri dari pemaparan materi dasar, eksplorasi framework terkini, dan workshop pembuatan website sederhana. Dilakukan evaluasi kepuasan peserta melalui kuisioner yang menunjukkan respon positif terhadap pemateri dan materi yang disampaikan. Hasil dan pembahasan menunjukkan bahwa kegiatan berlangsung lancar dan sukses, dengan partisipasi 50 peserta dari berbagai instansi. Feedback dari peserta menunjukkan kepuasan dan pemahaman yang baik terhadap materi.
Create Your Digital Identity: Bangun Portofolio Keren Dengan Bootstrap Hidayat, Ragil; Bintang, Reihan Aditya Permata; Irawan, Doni; Abean, Mohammad Arief; Filayati, Muhammad Albi Akbar; Sutrisno, Mirza; Mujiastuti, Rully; Adharani, Yana; Rosanti, Nurvelly; Ambo, Sitti Nurbaya
Dedication : Jurnal Pengabdian Masyarakat Vol 9 No 1 (2025): (In Progress)
Publisher : LPPM Universitas PGRI Argopuro Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31537/dedication.v9i1.2269

Abstract

Di era digital yang semakin maju, mempunyai identitas digital menjadi kebutuhan penting, terutama bagi mahasiswa. Salah satu cara untuk membangun identitas digital adalah dengan membuat portofolio berbasis website, dengan menggunakan framework sebagai bantuan dalam membangun portofolio website, seperti framework Bootstrap. Penulis dan tim mengadakan kegiatan mengenai membagun identitas digital dengan judul “Create Your Digital Identity: Bangun Portofolio Keren dengan Bootstrap”. Kegiatan ini dilaksanakan dengan metode pemaparan materi dengan webinar dan dilanjutkan dengan pelaksanaan praktik dengan workshop. Kepuasan peserta ditentukan dengan menggunakan kuesioner. Peserta memberikan respon positif terhadap pembicara dan materi yang disampaikan. Hasil dan diskusi menunjukkan bahwa kegiatan ini berhasil dibuktikan dengan partisipasi sebanyak 60 peserta dari berbagai lembaga. Peserta juga menyelesaikan Pre-Test dan Post-Test yang menunjukkan pemahaman yang baik terhadap materi tersebut. Feedback yang didapatkan dari peserta adalah 47,5 merasa puas dan 52,5% merasa sangat puas dengan kegiatan ini.
Educating on the Application of Tensorflow in Artificial Intelligence, Machine Learning and Deep Learning Santoso, Ilham Budi; Aji, Irfan Pandu; Franskusuma, Sutio; Putri, Khansa Aqila; Ardharani, Yana; Mujiastuti, Rully; Nurbaya Ambo, Sitti; Meilina, Popy; Rosanti, Nurvelly; Amri, Nurul
Society : Jurnal Pengabdian Masyarakat Vol 4, No 2 (2025): Maret
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v4i2.547

Abstract

In addition to bringing positive impacts, technological developments also provide new challenges in improving people's technological literacy, especially related to Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). One of the main challenges is the low public understanding of these technologies, which are increasingly relevant in the era of digital transformation. On the other hand, Google developed a library with the name TensorFlow which is widely used for data processing in Artificial Intelligence, Machine Learning, and Deep Learning. Based on this, educational activities were carried out in the form of introducing and training the use of TensorFlow to the general public in the form of webinars and workshops with the theme ‘Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning’. The activity was carried out in two stages, namely webinars for delivering basic material and workshops for hands-on practice. Based on evaluation through a Likert scale questionnaire, the majority of participants stated that they were very satisfied with the quality of the material, presenters, and implementation of activities. The post-test results also showed an increase in participants' understanding of the material, as evidenced by correct answers on topics such as TensorFlow functions, supervised learning, and neural networks. The participation of 52 participants from various institutions shows the success of this activity in achieving its goals.  
Utilization of Machine Learning for Property Price Segmentation and Prediction Andriansyah, Akbar; Dzulkarnain, Mulki Djenfik; Afkarinah, Afni Izzah; Amili, Fadel; Ramadhika, Gilang; rosanti, Nurvelly; Ambo, Siti Nurbaya; Andharani, Yana; Sutrisno, Mirza
Society : Jurnal Pengabdian Masyarakat Vol 4, No 2 (2025): Maret
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v4i2.537

Abstract

Advances in digital technology have encouraged the utilization of artificial intelligence, especially machine learning, in various sectors, including property price analysis. However, there are still many people who do not understand the basic concepts of this technology, so structured and applicable education is needed. To answer this challenge, an activity entitled “Utilization of Machine Learning for Property Price Segmentation and Prediction” was held which aimed to introduce and train participants in the application of machine learning to predict property prices. This activity consists of two main parts, namely webinars and workshops. The webinar focused on introducing the concepts of artificial intelligence, machine learning, and AI Project Cycle as the main method in analyzing house prices. Meanwhile, the workshop provided hands-on training to participants in building prediction models using Google Colab. This activity was carried out through a series of stages, starting from socialization, preparation of materials, pre-test to measure initial understanding, educational and practical sessions, to evaluation through post-test and filling in participant feedback. A total of 39 participants from various backgrounds participated in this activity. The evaluation showed that 38.7% of participants were satisfied, while 51.6% were very satisfied with the program. In addition, the post-test results showed a significant increase in understanding compared to the pre-test results. Based on these results, this activity proved to be successful in providing new insights into the application of machine learning in property price prediction and equipping participants with practical skills that can be applied in the real world.
Co-Authors Abduzzhohir, Yasir Abean, Mohammad Arief Abyan Shidqi, Muhammad Adharani, Yana Afkarinah, Afni Izzah Aji, Irfan Pandu Alfarizi, Muhammad Azis Alifsyah Ramadhan, Rafi Novranza Alriyanto, Abri Okta Ambiyah, Muhammad Chairil Ambo, Siti Nurbaya Amili, Fadel Amri, Nurul Andharani, Yana Andriansyah, Akbar Anjani, Meisya Putri Ardhani, Yana Ardharani, Yana Arista Putri, Syahda Asti Ayuningtyas, Asti Asyahri, Aldo Dwi Aullia, Mochammad Rizqi Azizah, Faza Shafa Bintang, Reihan Aditya Permata Budiarto, Khamdan Cholis Arethusa, Muhammad Daulay, Azhari Dewi Risanty, Rita Dzulkarnain, Mulki Djenfik Erwin Setiawan, Erwin Fadhlurokhman, Rafi Fadillah, Muhammad Daffa Fajri, Muhammad Akbar Al Fajri, Muhammad Ihsan Fakhul Jannah, Elsa Ananda Hanifah Faridzi, Salman Al Faza Shafa Azizah Filayati, Muhammad Albi Akbar Fizar, Bintang Al FJ, Elsa Ananda Hanjfah Franskusuma, Sutio Ghifari, Sandhi Alfianda Guritno, Deden Habibi, Yusuf Haryanda, Muhammad Haykal Andana, Muhammad Hendra Hendra Hidayat, Ragil Irawan, Doni Jumail, Jumail Khoirrosyid, Haida Khoirul Umam Lestari, Fina Dwi Mahmuda, Faiz Marhani Maududi, Izzuddin Al Qossam Maulana Saputra, Reza Ade Maulana, Mirza MUHAMMAD HASBI Muhammad, Hilbram Mukti, Hari Mustafa, Faizal Nindya Putri, Azzahra Novranza, Rafi Nurbaya Ambo, Sitti Popy Meilina Prayogo, Ikhsan Adi Putri, Khansa Aqila Putri, Syahda Arista Rabbani, Syamil Ghufron Rahmawati Gunawan, Alfiana Ramadhika, Gilang Retnani Latifah, Retnani Risanty, Rita Dewi Rizky Aditya, Fauzan Rizky Maulana, Rizky Rully Mujiastuti S, Muhamad Thirafi Qaedi Sabhan, Rifqi Baihaqi Saleh, Muhammad Raihan Saleh, Oldi Alfani Sunan Santoso, Ilham Budi Saputra, Agita Irvanda Saputra, Ambar Dwi Septiana, Dimas Sherli Fadilah, Ridha Shidqi, Muhammad Abyan Sirojul Munir Sitti Nurbaya Sunatun, Sunatun Sutrisno, Mirza Taufik Hidayat Vivria, Muhammad Efendi Wahyudi, Ramzy Al Firza Winnugroho Wiratman, Manfaluthy Hakim, Tiara Aninditha, Aru W. Sudoyo, Joedo Prihartono Zahro, Rahmita