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All Journal International Journal of Public Health Science (IJPHS) Journal of ICT Research and Applications ILKOM Jurnal Ilmiah Jiko (Jurnal Informatika dan komputer) Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Minfo Polgan (JMP) Inspiration: Jurnal Teknologi Informasi dan Komunikasi JEAT : Journal of Electrical and Automation Technology
Hamdan Gani
Politeknik ATI Makassar
Computer Science & IT Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Social Sciences

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ANALISIS KETERKAITAN DATA TRANSAKSI PENJUALAN BUKU MENGGUNAKAN ALGORITMA APRIORI DAN ALGORITMA CENTROID LINKAGE HIERARCHICAL METHOD (CLHM) Nurani, Nurani; Gani, Hamdan
ILKOM Jurnal Ilmiah Vol 9, No 1 (2017)
Publisher : Program Studi Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1280.548 KB)

Abstract

Pengambilan keputusan merupakan faktor yang menentukan didalam sebuah perusahaan. Kenyataannya pemanfaatan big data dalam sebuah pengambilan keputusan masih kurang efektif, salah satu contohnya adalah pemanfaatan big data perilaku belanja konsumen untuk menjadi sebuah pengetahuan yang mendukung pengambilan keputusan. Salah satu implementasi big data ini adalah pengambilan keputusan dalam penempatan barang pada rak toko buku. Berdasarkan permasalahan tersebut penelitian ini bertujuan mencari keterkaitan antara satu buku dengan buku yang lain didalam suatu set data dengan menggunakan teknik data mining, Penelitian ini memanfaatkan dua teknik data mining yaitu implementasi algoritma Apriori yang berfungsi untuk mendapatkan pola-pola item yang saling berkaitan kemudian algoritma CLHM (Centroid Linkage Hierarchical Method) untuk klasterisasi data. Penelitian ini menggunakan sampel data 15 kategori item dari data belanja konsumen pada toko buku tahun 2014 selama 3 bulan. Hasil akhir penelitian adalah sebuah pengetahuan baru yang dapat dijadikan rekomendasi pengambilan keputusan dalam sebuah toko buku
Using Customer Emotional Experience from E-Commerce for Generating Natural Language Evaluation and Advice Reports on Game Products Hamdan Gani; Kiyoshi Tomimatsu
Journal of ICT Research and Applications Vol. 13 No. 2 (2019)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2019.13.2.5

Abstract

Investigating customer emotional experience using natural language processing (NLP) is an example of a way to obtain product insight. However, it relies on interpreting and representing the results understandably. Currently, the results of NLP are presented in numerical or graphical form, and human experts still need to provide an explanation in natural language. It is desirable to develop a computational system that can automatically transform NLP results into a descriptive report in natural language. The goal of this study was to develop a computational linguistic description method to generate evaluation and advice reports on game products. This study used NLP to extract emotional experiences (emotions and sentiments) from e-commerce customer reviews in the form of numerical information. This paper also presents a linguistic description method to generate evaluation and advice reports, adopting the Granular Linguistic Model of a Phenomenon (GLMP) method for analyzing the results of the NLP method. The test result showed that the proposed method could successfully generate evaluation and advice reports assessing the quality of 5 game products based on the emotional experience of customers.
A Correlation Method for Meteorological Factors and Air pollution in association to covid-19 pandemic in the most affected city in Indonesia Nurilmiyanti Wardhani; Hamdan Gani; Sitti Zuhriyah; Helmy Gani; Etika Vidyarini
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.854.195-205

Abstract

This study aims to validate the correlation between meteorological factors and air pollution with the spread of Covid-19 in Jakarta, Indonesia. This study examined the Covid-19 cases of Jakarta and its five municipalities. The secondary data of Covid-19 cases, includes Daily Positive Cases (DPC) and Total Daily Positive Cases (TDPC), were retrieved from the Health Office of DKI Jakarta Province, while the meteorological and air pollution parameters were obtained from the online database archives. Kendall and Spearman rank correlation tests were used to analyze correlation between DPC and TDPC with meteorological and air pollution parameters. This study found that Air Quality Index and PM10 showed a significant positive correlation with DPC in municipalities of Jakarta. Also, the average air temperature was positively correlated to TDPC in all region of Jakarta. Average air temperature, Air Quality Index, and PM10 were the factors that take into account for the spread of Covid-19 pandemic in Jakarta, Indonesia. The warmer temperature associated to the higher number of case. Thus, there are no indications that the spread of Covid-19 in subtropical or temperate country may decrease when entering a warmer season that resembles the climatic characteristics in tropical region. Additionally, the significance of air pollutant factors implies that reducing air pollution should be promoted as it might reduce the spread of Covid-19. The findings of this study would be useful to support the strategy and policy in preventing the spread of Covid-19 in the country.
Dengue Hemorrhagic Fever Incidence in Indonesia Using Trend Analysis and Spatial Visualization Helmy Gani; Rizky Maharja; Hamdan Gani; Nurilmiyanti Wardhani; Nurani Nurani; Nur Fadhilah Gani; Muhammad Akbar Salcha; Riadnin Maharja
International Journal of Public Health Science (IJPHS) Vol 11, No 4: December 2022
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v11i4.21533

Abstract

Dengue hemorrhagic fever (DHF) has caused a public health risk in many developing countries, so understanding their incidence trend is needed to prepare an early warning prevention.The multi-year DHF trend analyses are necessary but have not been accomplished to this day in Indonesia. This study examined time trends using yearly data on the incidences of DHF for all provinces. Univariate forecasting model constructed on the data up to 2019 predicted the future trends in the disease's incidences up to 2022. At the same time, a trend analysis test was developed to explain the disease trend for all regions. The results per province showed a declining trend of DHF cases and TDC in Java Island (i.e., Jawa Barat, Jawa Tengah, DKI Jakarta, and Banten). Then, there was an increasing trend in the majority of regions outside of Java Island. For the CFR and IR, most provinces had decreasing trend except for Gorontalo, Kalimantan Utara, and Maluku. Overall, trend analysis showed a continually decreasing trend of DHF, TDC, CFR, and IR incidence over the past 16 years in Indonesia. The findings highlight the need for preventive policies for several provinces with the increasing trend of DHF incidences.
ANALISIS KESEGARAN IKAN MUJAIR DAN IKAN NILA DENGAN METODE CONVOLUTIONAL NEURAL NETWORK Cakra Cakra; Syafruddin Syarif; Hamdan Gani; Andi Patombongi; Andi Muh Islah
Simtek : jurnal sistem informasi dan teknik komputer Vol. 7 No. 2 (2022): Oktober 2022
Publisher : STMIK Catur Sakti Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51876/simtek.v7i2.138

Abstract

Dalam riset ini, kami melakukan eksperimen implementasi klasifikasi kesegaran ikan mujair dan ikan nila (segar dan tidak segar) berdasarkan mata ikan menggunakan transfer learning dari enam CNN, yaitu Resnet, Alexnet, Vgg-16, Squeezenet, Densenet dan Inception. Dari hasil eksperimen klasifikasi dua kelas kesegaran ikan mujair menggunakan 451 citra menunjukkan bahwa VGG mencapai kinerja terbaik dibanding arsitektur lainnya dimana akurasi klasifikasi mencapai 73%. Dengan akurasi lebih tinggi dibanding arsitektur lainnya maka Resnet relatif lebih tepat digunakan untuk klasifikasi dua kelas kesegaran ikan Mujair, sedangkan ikan nila dengan menggunakan 574 citra menunjukkan bahwa VGG mencapai kinerja lebih baik dibanding arsitektur lainnya dengan akurasi klasifikasi mencapai 57,9%, dengan demikan maka VGG relatif lebih tepat digunakan untuk klasifikasi dua kelas kesegaran ikan Nila.
Perancangan Prototype Monitoring Kadar Oksigen Dalam Darah untuk Penghuni Panti Werdha Theodora Makassar Berbasis IOT Menggunakan Modul ESP8266 Tamsir; Yuyun; Hamdan Gani; Andi Nur Fadillah; Asrul
Jurnal Minfo Polgan Vol. 12 No. 2 (2023): Artikel Penelitian 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v12i2.13102

Abstract

SpO2 adalah Kadar oksigen dalam darah yang harus selalu dijaga agar tetap normal dengan nilai saturasi diantara 95-100%, kadar oksigen dalam darah yang kurang dari 95% dapat menimbulkan berbagai macam penyakit dan membutuhkan alat bantu pernafasan disebabkan kurangnya oksigen di dalam darah dan jika terlambat ditangani maka dapat mengakibatkan kematian, kondisi ini dapat terjadi pada lansia dengan gejala hipoksemia. Untuk mengantisipasi terjadinya hiposemia maka dilakukan penelitian perancangan prototipe monitoring kadar oksigen dalam darah untuk penghuni panti werdha Theodora Makassar berbasis IOT menggunakan modul ESP8266 dengan tujuan mengontrol perubahan kadar oksigen dalam darah setiap saat dan dapat dimonitoring dari jarak jauh menggunakan server Ubidots dengan sistem yang terhubung ke jaringan sehingga perawat di panti dapat memonitoring SpO2 lansia dimanapun berada, apabila terjadi SpO2 tidak normal maka segera diambil tindakan penanganan pada lansia. Sistem ini dirancang dengan menggunakan sensor MAX30100 sebagai pendeteksi kadar oksigen dalam darah yang terhubung dengan NodeMCU ESP8266. ketika sensor mendeteksi kadar oksigen < 95% maka akan dikirim notifikasi ke perawat panti dan indicator di server Ubidots akan berwarnah merah, Pengujian alat dicobakan kebeberapa lansia secara berurutan dan dilakukan pengamatan pada server Ubidots dengan metode metric dan gauge pada server Ubidots, alat yang dirancang dapat bekerja dengan baik dan terbukti akurat dan efisien.
Analysis Of Themes and Trends in Life Sciences and Biomedical Research Virtual Reality Sahibu, Supriadi; Munsyir, Mulawarman; Gani, Hamdan; Taufik, Imran; Iskandar, Akbar
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 12 No. 2 (2022): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (786.578 KB) | DOI: 10.35585/inspir.v12i2.5

Abstract

The purpose of this study was to investigate the theme of the abstract, the theme of the Title on Virtual Reality in the Sciences and Biomedical Sciences and to investigate the trends of authors and countries who are major contributors to research on virtual reality in the sciences and biomedical sciences, the researchers used metadata from 497 journals. scientifically indexed with topic modeling algorithms. The results of the study found that for virtual reality themes listed in the abstracts of science and biomedical fields, the words that often appear were study and systems related to pediatric surgery, stroke and cancer. Then the research theme in the virtual reality title regarding science and biomedicine is to get words that often appear, namely evaluation and videos that contain about the fields of dental health, stroke and cancer, Furthermore, from a technological point of view, it relates to head-mounted devices to display 2 or 3 dimensions, and educational psychometric technology relates to learning to care for children for students. While the sports side is related to the movement of the body's physical activity in adults. Analysis of research trends for authors and countries that are major contributors to virtual reality research on science and biomedical based on wordcloud analysis with names that often appear in virtual reality is Wiederhold BK and the dominant country is United State.
Machine Learning and Internet of Things (IoT): A Bibliometric Analysis of Publications Between 2012 and 2022 Gani, Hamdan; Damayanti, Annisa Dwi; Nurani, Nurani; Zuhriyah, Sitti; Jabir, St. Nurhayati; Gani, Helmy; Zhipeng, Feng; Rejeki, Aisyah Sri
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1700.27-37

Abstract

The implementation between machine learning and the Internet of Things (IoT) has been scientifically investigated in many studies. However, not many bibliometric studies categorize the output in this area. By keeping an eye on the publications posted on the Web of Science (WoS) platform, this study aims to give a bibliometric analysis of research on Machine Learning and IoT, identifying the state of the art, trends, and other indicators. 6.170 different articles made up the sample. The VOS viewer software was used to process the data and graphically display the results. The study examined the concurrent occurrence of publications by year, keyword trends, co-citations, bibliographic coupling, and analysis of co-authorship, countries, and institutions. several prolific authors are discovered. However, the body of literature on machine learning and IoT issues is expanding quickly; only five papers accounted for more than 2193 citations. Then, 40.34 percent of the articles from the 694 sources reviewed were published as the most important paper. At the same time, the USA is the top nation for research on this subject area. In addition to identifying gaps and promising areas for future research, this study offers insight into the current state of the art and the field of machine learning and IoT.
Weather Prediction for Strawberry Cultivation Using Double Exponential Smoothing and Golden Section Optimization Methods Herlinah, Herlinah; Asrul, Billy Eden William; HS, Hafsah; Faisal, Muhammad; Lee, Swa Lee; Gani, Hamdan; Feng, Zhipeng
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2290.305-317

Abstract

Strawberry is one of the fruit commodities that has a high demand so that it is widely cultivated by most people in Bantaeng Regency to meet with the market needs. The high intensity of weather changes is the main challenge in the strawberry production, which is influenced by climate dynamics and the start season time changes. Climate change does not only affect the amount of rainfall, but also causes a shift in the rainy season and dry season start. As a result, in the cultivation of plants such as strawberries, there are often difficulties in adjusting or slow anticipation in the extreme changes of rainfall. This research began with the data collection stage through field observations, interviews, and literature studies. The design tool used a systematically organized UML, which included a use case diagram, then an activity diagram, as well as an elaboration into sequence diagrams, and class diagrams. The system was developed by implementing the PHP programming language on the interface design as well as MySQL as a database processing. The algorithm used to predict the air temperature feature, wind speed feature, and rainfall feature was Double Exponential Smoothing, followed by the optimization of the Golden Section method to select the right smoothing value. Referring to the results of this study, the system can provide planting time recommendations based on prediction of rainfall, air temperature, and wind speed parameters through a web-based platform. Based on the calculation of the accuracy value of the prediction results using the Mean Absolute Percentage Error (MAPE), the obtained forecast error value was of 5.89% for wind speed, 0.63% for air temperature, and 0.69% for rainfall. The Golden Section Optimization in Double Exponential Smoothing provided the best smoothing for prediction.
EVALUATION OF INDOBERT AND ROBERTA: PERFORMANCE OF INDONESIAN LANGUAGE TRANSFORMER MODELS IN SENTIMENT CLASSIFICATION Nur, M. Adnan; Umar, Najirah; Feng, Zhipeng; Gani, Hamdan
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 2 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.9988

Abstract

The development of Natural Language Processing (NLP) technology has had a significant impact on various fields, especially in sentiment analysis. This analysis becomes important in understanding public perception, especially on social media which has a lot of opinions. Indonesian, with its morphological complexity, dialectal variations, and dynamic everyday vocabulary usage, presents unique challenges in the development of NLP models. This study aims to evaluate and compare the performance of two Indonesian language transformer models, namely IndoBERT (Indonesia Bidirectional Encoder Representations from Transformers) and RoBERTa Indonesia (Robustly Optimized BERT Pretraining Approach) in applying sentiment classification using the Indonesian General Sentiment Analysis Dataset. Both models were fine-tuned using consistent hyperparameter configurations to ensure the validity of the comparison. Evaluation was conducted based on classification metrics, namely precision, recall, F1-score, and accuracy. The results show that the IndoBERT model excels in all aspects of evaluation. IndoBERT achieved an accuracy of 70%, while RoBERTa Indonesia only reached 67%. Additionally, the average F1-score of IndoBERT at 0.69 is higher compared to RoBERTa, which only reached 0.65. The performance of IndoBERT is also more balanced in classifying the three sentiment categories (negative, neutral, and positive), whereas RoBERTa shows less consistent performance, especially in negative and positive sentiments. In the loss analysis, IndoBERT produced a lower evaluation loss value, indicating better generalization capability. Additionally, IndoBERT also shows faster and more stable training times compared to RoBERTa. This performance difference shows that the architecture and pre-trained data used by each model affect their ability to understand Indonesian contextually. This research provides a comprehensive comparative overview of the effectiveness of two transformer models in the task of Indonesian language sentiment analysis, as well as lays the groundwork for selecting a more optimal model in the development of NLP systems for social media.
Co-Authors Akbar Iskandar Amhar Nasir, Muhammad Andi Muh Islah Andi Nur Fadillah Andi Patombongi Asrul Billy Eden William Asrul Cakra Cakra Damayanti, Annisa Dwi Etika Vidyarini Feng, Zhipeng Gani, Helmy Gani, Helmy Herlinah Herlinah HS, Hafsah Jabir, St. Nurhayati Kiyoshi Tomimatsu Mubarak, Muhammad Muflih Muhammad Akbar Salcha Muhammad Faisal Muhammad Ibrar Aprianto Munsyir, Mulawarman Najirah Umar Nur Fadhilah Gani Nur, M. Adnan Nurani Nurani Nurani Nurani Rejeki, Aisyah Sri Riadnin Maharja Rizky Maharja Sahibu, Supriadi Sitti Wetenriajeng Sidehabi Syafruddin Syarif Tamsir Taufik, Imran Wardhani, Nurilmiyanti Yuyun Yuyun, Yuyun Zhipeng, Feng Zuhriyah, Sitti