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All Journal JOIN (Jurnal Online Informatika) Jurnal Mirai Management JOIV : International Journal on Informatics Visualization JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Teknoinfo JSiI (Jurnal Sistem Informasi) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat IJIES (International Journal of Innovation in Enterprise System) Jurnal Teknologi Informasi dan Pendidikan Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Indonesia : Manajemen Informatika dan Komunikasi JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) Prosiding Konferensi Nasional PKM-CSR eProceedings of Engineering Jurnal INFOTEL Journal of Community Service and Society Empowerment Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
Faqih Hamami
Telkom University, Bandung
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Nursing Public Health Social Sciences Other

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KLASIFIKASI CUACA PROVINSI DKI JAKARTA MENGGUNAKAN ALGORITMA RANDOM FOREST DENGAN TEKNIK OVERSAMPLING faqih hamami; Iqbal Ahmad Dahlan
Jurnal Teknoinfo Vol 16, No 1 (2022): Januari
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v16i1.1533

Abstract

Saat ini Indonesia sering mengalami perubahan cuaca ekstrem yang menyebabkan banyak bencana seperti banjir, kebakaran, longsor dan badai. Jenis cuaca bergantung dari banyak faktor seperti suhu, kelembaban, arah angin dan lainnya. Beberapa kegiatan manusia bergantung terhadap perubahan cuaca seperti di sektor pertanian, perkebunan, penerbangan, daerah tinggi dan pantai. Prediksi cuaca menjadi penting untuk lebih memahami perubahan cuaca ekstrem yang didasarkan dari faktor cuaca. Penelitian ini mengadopsi ensemble learning yang mampu melakukan klasifikasi cuaca dengan baik. Algoritma yang digunakan adalah Random Forest yang dikombinasikan dengan teknik oversampling untuk menangani ketidakmerataan jumlah data dari setiap kelas cuaca. Beberapa kategori cuaca yang diklasifikasikan adalah Cerah, Cerah Berawan, Berawan, Berawan Tebal, Hujan Lokal, Hujan Ringan, Hujan Sedang dan Hujan Petir. Hasil eksperimen diperoleh bahwa model Random Forest mencapai akurasi 70%.  Teknik oversampling yang digunakan adalah metode Synthetic Minority Over-sampling Technique (SMOTE). Dengan kombinasi SMOTE prediksi dari setiap kelas minoritas dapat ditingkatkan dengan rata-rata sebesar 50%
WORKSHOP PEMROGRAMAN WEB UNTUK SISWA DAN GURU SMA DI KOTA BANDUNG Rahmat Fauzi; Iqbal Santosa; Faqih Hamami
J-Dinamika : Jurnal Pengabdian Masyarakat Vol 6 No 2 (2021): December
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-dinamika.v6i2.2435

Abstract

Workshop Pemrograman Web Untuk Siswa dan Guru SMA di Kota Bandung ini bertujuan untuk membangun logika berpikir secara sistematis dan terstruktur dalam bentuk aplikasi/situs web dengan teknik pengkodean yang baik dan benar. Kegiatan ini diharapkan mampu membuat peserta memahami pentingnya pemrograman web, mengenal teknologi yang digunakan dan menumbuhkan minat dalam pemrograman web. Peserta kegiatan ini adalah perwakilan siswa dan guru SMA Alfa Centauri Kota Bandung. Metode pelaksanaan workshop yang digunakan ialah dengan mengkombinasikan peragaan langsung, praktik langsung, dan diskusi interaktif. Peserta dibagi dalam beberapa kelompok kecil yang dibimbing oleh para tutor untuk memahami konsep pemrograman dan mempraktikkannya secara langsung. Konsep dipecah dalam beberapa bagian, dan setiap bagian diakhiri dengan latihan yang kemudian diulas hasilnya. Alat bantu pelaksanaan workshop yang digunakan ialah aplikasi pertemuan online dan situs web kursus online. Peserta mengikuti workshop secara daring menggunakan zoom, sambil mengakses materi dan bahan workshop pada situs web yang telah disiapkan.
Real-time passenger social distance monitoring with video analytics using deep learning in railway station Iqbal Ahmad Dahlan; Muhammad Bryan Gutomo Putra; Suhono Harso Supangkat; Fadhil Hidayat; Fetty Fitriyanti Lubis; Faqih Hamami
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp773-784

Abstract

Recently, at the end of December, the world faced a severe problem which is a pandemic that is caused by coronavirus disease. It also must be considered by the railway station's authorities that it must have the capability of reducing the covid transmission risk in the pandemic condition. Like a railway station, public transport plays a vital role in managing the COVID-19 spread because it is a center of public mass transportation that can be associated with the acquisition of infectious diseases. This paper implements social distance monitoring with a YOLOv4 object detection model for crowd monitoring using standard CCTV cameras to track visitors using the DeepSORT algorithm. This paper used CCTV surveillance with the actual implementation in Bandung Railway Station with the accuracy at 96.5 % result on people tracking with tested in real-time processing by using minicomputer Intel(R) Xeon(R) CPU E3-1231 v3 3.40GHz RAM 6 GB around at 18 FPS.
Analisis Sentimen IMBd Film Review Dataset Menggunakan Support Vector Machine (SVM) dan Seleksi Feature Importance Hilda Nuraliza; Oktariani Nurul Pratiwi; Faqih Hamami
Jurnal Mirai Management Vol 7, No 1 (2022)
Publisher : STIE AMKOP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/mirai.v7i1.2222

Abstract

Perkembangan teknologi internet khususnya dalam bidang perfilman memberikan sarana terbuka bagi masyarakat dalam mengekspresikan pendapat dan emosional. Salah satu pendapat yang seringkali masyarkat keluarkan yaitu berupa penilaian sebuah film pada platform tertentu seperti IMDB. Ulasan yang dikeluarkan tentunya mengandung emosional yang dibawakan oleh masyarakat itu sendiri, baik emosional positif maupun negatif yang dinamakan sentimen. Sentimen atau opini masyarakat ini perlu dianalisis untuk mengklasifikasikan opini sesuai dengan kelasnya sehingga kecenderungannya terhadap suatu objek dapat diketahui. Adapun metode yang digunakan dalam penelitian ini yaitu metode data mining dengan Knowledge Discovery in Database (KDD). Tujuan dari penelitian ini yaitu analisa sentiment IMDB film review oleh masyarakat menggunakan algoritma Support Vector Machine dan seleksi Feature importance untuk memperoleh pola dan hasil yang terbaik. Dengan pengujian validasi akurasi data menggunakan metode split data sederhana dan k-fold cross validation yang menghasilkan akurasi sebesar 91.942% dan 87.699%. Lalu Kemudian dilakukan evaluasi menggunakan confusion matrix dengan penetapan max feature sebesar 10000 untuk memeriksa nilai ketepatan prediksi yang dilakukan oleh model yaitu diperoleh akurasi sebesar 88.033%. Dalam hal ini dapat dibuktikan bahwa kemampuan model dalam melakukan klasifikasi dinilai cukup baik. Keywords: Data Mining, KDD, Feature Importance, SVM, Confusion Matrix
Pengembangan Sistem Penyiraman Otomatis Tanaman Anthurium Berbasis IoT Dimas Raihan Zein; Faqih Hamami; Tatang Mulyana
Journal of Information System Research (JOSH) Vol 4 No 1 (2022): October 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (536.659 KB) | DOI: 10.47065/josh.v4i1.2301

Abstract

Ornamental plants are types of plants that are cultivated to provide additional aesthetic value. One of the commercial ornamental plants known in Indonesia is the wave of love or Anthurium plowmanii. This type of Anthurium has a high selling value because of special care, one of which is regular and not excessive water supply. One of the technologies that can help humans in automatically watering anthurium plants is the Internet of Things (IoT). ThingSpeak IoT platform connected with NodeMCU ESP8266 is used as artificial intelligence to control and determine watering time, watering automatically takes place when plants need air. This research was conducted to design an IoT-based automatic anthurium plant watering system and collect data on soil moisture and anthurium temperature from the installed IoT sensors. The method in this research is prototyping which is used to produce certain products, and test the effectiveness of these products. This IoT-based automatic watering system for anthurium plants is calibrated by making comparisons with standard measuring instruments to determine the accuracy of the sensor measurement results. The results of this study are a prototype of an IoT-based automatic watering system for anthurium plants by achieving an average level of sensor accuracy in reading anthurium plant conditions reaching 98.1% for soil moisture sensors while for temperature sensors reaching 98.8%. This research is expected to help farmers and ornamental plants communities to facilitate automatic watering process according soil moisture and temperature.
Implementasi Logika Fuzzy Untuk Pendukung Keputusan Sistem Penyiraman Otomatis Tanaman Anthurium Dina Meliana Saragi; Faqih Hamami; Tatang Mulyana
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 1 (2022): September 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4895

Abstract

Anthurium is a class of ornamental plants that are admired by many lovers of ornamental plants, this plant is cultivated on a wide scale in the floriculture industry. There are factors that support the current high price of anthurium plants, first, a unique species with a ratio of 10% of anthurium seeds that grow exactly the same as the parent. In addition, anthurium growth is very slow and difficult to care for. Other factors must be considered in the cultivation of anthurium plants, namely air temperature, humidity, sunlight, acidity (pH) and water requirements. This anthurium plant is a plant that is sensitive to water so it requires supervision of regular watering so that the plant does not die. Farmers need advanced expert knowledge in making different decisions related to agriculture, especially in dosing and timing of crop watering. Therefore, in this study, researchers designed fuzzy logic according to the needs of anthurium plants with a rule base that can change IoT sensor data in the form of DHT11 sensors and Soil Moisture Sensors FC-28 into the output of a decision on the duration of plant watering. In this stage, the process of fuzzification, inference and defuzzification. The results obtained during this research are comparative testing of 15 values from the output devices that are taken at random approximately closer to the values from the simulation with MATLAB with a total difference of 8.61% due to the difference in calculations between IoT devices and simulations with MATLAB, but this can still be categorized accurately because the output results of the MATLAB tool and simulation are still within the range of membership function values.
PERBANDINGAN KINERJA ALGORITMA RECURRENT NEURAL NETWORK (RNN) DAN LONG SHORT-TERM MEMORY (LSTM): STUDI KASUS PREDIKSI KEMACETAN LALU LINTAS JARINGAN PT XYZ Silmy Sephia Nurashila; Faqih Hamami; Tien Fabrianti Kusumasari
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 3 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i3.3961

Abstract

Peningkatan perkembangan teknologi sebanding dengan peningkatan penggunaan internet. Kualitas jaringan internet di Indonesia yang masih rendah dengan peningkatan penggunaan jaringan yang terus bertambah menyebabkan kemungkinan terjadinya kemacetan jaringan lebih tinggi. Penggunaan internet yang tinggi menyebabkan seringkali terjadi kemacetan jaringan yang menyebabkan penurunan kualitas dan performa jaringan. Pada penelitian ini akan dilakukan prediksi lalu lintas jaringan. Terdapat beberapa model algoritma yang dapat digunakan untuk melakukan prediksi dan penelitian ini akan dibahas mengenai perbandingan performa dari model Long Short-Term Memory (LSTM) dan Recurrent Neural Network (RNN). Dari hasil perbadingan diperoleh bahwa LSTM memiliki nilai yang lebih tinggi dalam performa dengan mendapatkan nilai akurasi R-Squard sebesar 99.2% jika debandingkan dengan model dengan algoritma RNN yang memiliki nilai akurasi R-Squard sebesar 99,1%. Manfaat dari penelitian ini yaitu untuk menguji performa dari model LSTM dan RNN terhadap dataset PT XYZ. Penelitian ini diharapkan dapat memberikan referensi kepada pengembang model Deep Learning untuk pengembangan kedepannya agar mendapatkan hasil yang lebih baik.
PENERAPAN ALGORITMA TF-IDF DAN NAÏVE BAYES UNTUK ANALISIS SENTIMEN BERBASIS ASPEK ULASAN APLIKASI FLIP PADA GOOGLE PLAY STORE Sheva Aditya Helmayanti; Faqih Hamami; Riska Yanu Fa’rifah
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.415

Abstract

The development of the internet has changed people's lifestyle with the existence of FinTech. One of the popular FinTech innovations is the Flip digital wallet application. In this study, aspect-based sentiment analysis was carried out on Flip user reviews using the naive bayes algorithm. The test results show high accuracy, with an average accuracy of 0.84. The naive bayes algorithm is effective in classifying user reviews based on aspects of speed, security, and cost, with accuracies of 0.80, 0.87, and 0.84, respectively. This research provides important insights for service providers to improve service performance and innovation. The labelling data generated the most sentiment 0 (no sentiment), followed by sentiment 1 (positive) and 2 (negative). Negative sentiments have a high frequency on speed and security aspects, while positive sentiments have a high frequency on cost aspects. Thus, improvements are needed to the security system and speed of the Flip application to increase user satisfaction in these aspects. The naive bayes algorithm can be a useful tool in processing review data on e-wallet applications and similar services.
ASPECT-BASED SENTIMENT ANALYSIS TERHADAP ULASAN APLIKASI FLIP MENGGUNAKAN PEMBOBOTAN TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) DENGAN METODE KLASIFIKASI K-NEAREST NEIGHBORS (K-NN) Ferda Ayu Dwi Putri Febrianti; Faqih Hamami; Riska Yanu Fa’rifah
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.429

Abstract

The rapid growth of online transactions in Indonesia has increased the demand for efficient interbank transfer solutions. However, the costs associated with such transactions have become a significant obstacle. Flip, a company with a vision to become a global leader in customer satisfaction-driven services, offers a solution to this challenge. This study proposes an aspect-based sentiment analysis method using the K-Nearest Neighbors (K-NN) algorithm to analyze user sentiment on key aspects, namely speed, security, and the cost of using the Flip application. The results of this research provide valuable information that can be used as a basis to provide insights, suggestions and recommendations to businesses, so they can create better solutions and promote optimal user experience. The research results show that the K-NN model has the ability to predict user psychology well in all aspects, with a significant level of accuracy, specifically speed (73.04%), security (86, 05%) and costs (80.11%). In addition, this study also compares two model validation methods: simple data splitting method and K-Fold cross-validation. Although the simple data splitting method has a higher average accuracy, K-fold cross-validation is considered superior as it provides a more accurate and reliable estimate of the overall performance of the model. Sentiment analysis results show that Flip app users tend to give negative feedback on speed and security, while they give positive feedback on cost. Therefore, the main recommendation is that the company PT Fliptech Lentera Inspirasi Pertiwi improves the speed and security aspects to increase user satisfaction with the Flip application. Therefore, this customer-centric service will continue to prioritize user satisfaction as its primary goal.
Aspect-Based Sentiment Analysis On FLIP Application Reviews (Play Store) Using Support Vector Machine (SVM) Algorithm Nurul Hidayati; Faqih Hamami; Riska Yanu Fa’rifah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 1 (2023): Issues July 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i1.9768

Abstract

The development of fintech has driven the rapid growth of e-wallets like Flip, offering a convenient solution for interbank transfers without administrative fees. User reviews on the Play Store serve as crucial feedback for understanding the user experience. This research utilizes aspect-based sentiment analysis (ABSA) in combination with the SVM method to detect opinions, perceptions, and reviews pertaining to Flip's speed, security, and cost aspects. The objective is to provide valuable insights to both users and companies regarding their experiences with Flip in conducting financial transactions. The study employs a dataset comprising 13,500 preprocessed and cleansed data points, followed by TF-IDF vectorization. The data is divided into training and testing sets, utilizing techniques such as the train-test split and K-Fold Cross Validation to assess model performance. GridSearch analysis reveals that specific parameter combinations, notably C=1.0 and test_size=0.1, yield high accuracy across all aspects, with the linear kernel displaying the highest overall accuracy. Model evaluation is conducted using the confusion matrix and classification report, presenting accuracy, precision, recall, and F1-scores for each aspect. Notably, the Support Vector Machine model performs well, particularly in the speed, security, and cost aspects, where the cost aspect demonstrates exceptionally strong results. In summary, this study employs ABSA to analyze Flip application reviews, with the Support Vector Machine model showcasing impressive performance across various aspects, providing valuable insights for users and companies engaging with Flip's financial transaction services.Keywords: aspect-based sentiment analysis, support vector machine, reviews, Flip
Co-Authors Agus Maolana Hidayat Ahmad, Mokhtarrudin Al amudi, Farhan Hasan Aldi Akbar Ambarita, Ruth Sesilya Anis Farihan Mat Raffei Arrahmani, Farras Hilmy Aziz, Abdurrahman Azzam Imaduddin, Muhammad Budi Rustandi Kartawinata Dahlan, Iqbal Ahmad Deandra, Valen Deden Witarsyah Dimas Raihan Zein Dina Meliana Saragi Fa'rifah, Riska Yanu Fabrianti Kusumasari, Tien Fadhil Hidayat Faishal Mufied Al Anshary Febrianti, Ferda Ayu Dwi Putri Ferda Ayu Dwi Putri Febrianti Ferda Ernawan Fetty Fitriyanti Lubis Firzania, Heidea Yulia Fitri Bimantoro Hadwirianto, Muhammad Raihan Helmayanti, Sheva Aditya Hidayati, Ilma Nur I Gede Pasek Suta Wijaya Iqbal Santosa Irfan Darmawan Ismail, Mohd Arfian Jauhari, M.Habib Joel Rayapoh Damanik Kardila, Yuni Kurniawan, Muhammad Rayhan Kuswandi, Brillian Adhiyaksa Lubis, Rizki Aulia Akbar Mangsor, Miza Mardika, Jody Mat Raffei, Anis Farihan Maulana, Fakhri Hassan Muhammad Bryan Gutomo Putra Muhammad Fahmi Hidayat, Muhammad Fahmi Muhammad Fauzan Nasrullah Muhammad Hafizh Murahartawaty Murahartawaty Nasrullah, Muhammad Fauzan Nicolaus Advendea Prakoso Indaryono Novanza, Alvin Renaldy Nuraliza, Hilda Nurul Hidayati Nuryatno, Edi Oktariani Nurul Pratiwi Orvalamarva Pratiwi, Oktaria Nurul Puruhita, Maretha Fitrie Puspitasari, Aprilia Mega Rachmadita Andreswari Raffei, Anis Farihan Mat Rahmah, Najma Syarifa Rahmat Fauzi Ramdani, Dwi Fickri Insan Ramli, Muhammad Ayyub Razali, Raja Razana Raja Rd. Rohmat Saedudin Salsabila Riswanti, Khairunnisa Satya Nugraha, Gibran Sheva Aditya Helmayanti Silmy Sephia Nurashila Sinung Suakanto Suhono Harso Supangkat Sujak, Aznul Fazrin bin Abu Syfani Alya Fauziyyah Tatang Mulyana Tien Fabrianti Kusumasari Vina Fadillah Widyadhari, Dinda Putri Yanu Fa'Rifah, Riska Yudo Husodo, Ario Yulizar, Iqbal Zahid, Azham