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"Pada penelitian ini membahas sistem pemantauan pada stairlift menggunakan internet of things (IoT), di mana sistem tertanam dalam fisik stairlift menggunakan sensor yang dipasang pada komponen stairlift dan kemudian diintegrasikan ke dalam platform IoT cloud (thingspeak) melalui jaringan internet. Akuisisi data fisis multi-sensor dapat berjalan, banyak informasi yang dapat diakses seperti: temperature motor, kecepatan, beban penumpang, konsumsi daya, getaran bearing dan getaran motor. Sistem pemantauan dapat berjalan secara real time, sehingga membuat pemantauan terpusat dan kegagalan operasi stairlift dapat dicegah sedini mungkin melalui early warning system (EWS) via Telegram. Selain itu, sistem ini dapat memberikan dukungan analisis teknis dalam mengembangkan prototype stairlift di masa mendatang. Berdasarkan analisis hasil pemantauan yang diperoleh, prototype stairlift layak dikembangkan untuk skala industri, secara operasional memenuhi ASME A18.1, ISO 10816 dan ISO 2372. Hal ini ditunjukkan dalam ujicoba variasi beban penumpang hingga maksimum 115 kg diperoleh kecepatan maksimum rata-rata <0,2 m/s, temperature motor <74,6 ˚C, konsumsi daya <600 watt, acceleration getaran bearing <0,5 g'peak dan kecepatan getaran motor (RMS) <4,5 m/s. Namun masih dibutuhkan improvement pada sistem teknis operasional prototype stairlift diantaranya temperature motor, konsumsi daya dan kecepatan agar dapat berjalan stabil.

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This research discusses monitoring systems on stairlift using internet of things (IoT), where the system embedded in the physical stairlift uses sensors that are mounted on the stairlift component and then integrated into the IoT cloud platform (thingspeak) via the internet network. Multi-sensor physical data acquisition can run, a lot of information that can be accessed such as: motor temperature, speed, passenger load, power consumption, bearing vibration and motor vibration. The monitoring system can run in real time, thus making centralized monitoring and failure of stairlift operations preventable as early as possible through the early warning system (EWS) via Telegram. In addition, this system can provide technical analysis support in developing stairlift prototypes in the future. Based on the analysis of the monitoring results obtained, the prototype stairlift is suitable for industrial scale development, operationally compliant with ASME A18.1, ISO 10816 and ISO 2372. This is shown in the trial of passenger load variations up to a maximum of 115 kg obtained an average maximum speed <0, 2 m/s, motor temperature <74.6˚C, power consumption <600 watts, bearing vibration acceleration <0.5 g'peak and motor vibration speed (RMS) <4.5 m/s. However, improvements are still needed in the operational technical system of the prototype stairlift including motor temperature, power consumption and speed so that it can run stably."

"Gempa bumi, salah satu bencana alam yang sering terjadi di Indonesia dan sering memakan banyak korban jiwa. Sistem peringatan dini terbukti efektif untuk mengurangi jumlah korban jiwa akibat gempa bumi ataupun bencana susulan lainya. Perangkat Internet-of-things berbasis seluler dapat digunakan sebagai perangkat peringatan dini untuk mendeteksi gempa bumi.Ketika banyak perangkat pendeteksi kejadian secara bersamaan melakukan transmisi ke jaringan seluler maka akan terjadi bursty transmission yang mengakibatkan kongesti pada jaringan seluler. Kongesti yang terjadi mengakibatkan peningkatan delay dan penuruan success probability pada prosedur random access jaringan seluler yang dapat mengurangi efektifitas sistem deteksi dini. Pengaturan preamble pada access class dapat digunakan untuk mengurangi dampak kongesti yang terjadi.

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Earthquakes, one of the natural disasters that often occur in Indonesia and often take many lives. The early warning system has proven to be effective in reducing the number of casualties due to earthquakes or other aftershocks. Cellular-based Internet-of-things devices can be used as early warning devices to detect earthquakes. When multiple incident detection devices are simultaneously transmitting to the cellular network, bursty transmission will occur, resulting in congestion on the cellular network. The congestion that occurs results in an increase in delay and a decrease in success probability in random access cellular network procedures which can reduce the effectiveness of the early detection system. Preamble settings on access classes can be used to reduce the impact of congestion that occurs."

"[ABSTRAKTesis ini berjudul Peran Pre-Emtif Intelijen Keimigrasian Sebagai Early Warning Information Terhadap People Smuggling. Dalam tesis ini, penulis mencoba mendiskripsikan bagaimanakan peran Intelijen Keimigrasian dalam menghasilkan produk intelijen berupa peringatan dini terhadap people smuggling. Bagaimanakan proses penghasilan peringatan dini tersebut serta bagaiman implementasi dari peringatan dini tersebut. Selain itu pula penulis mencoba untuk mendeskripsikan upaya-upaya apa saja yang dapat dilakukan oleh Intelijen Keimigrasian dalam mencegah people smuggling tersebut. Teknik penulisan tesis ini menggunakan metode kualitati deskriftif, sehingga dalam melakukan analisisnya penulis berusaha untuk menruraikan dalam bentuk tulisan dimana sebelumnya data diperoleh dari wawancara dan studi pustaka.

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ABSTRACTThis thesis entitled ?Peran Pre-Emtif Intelijen Keimigrasian Sebagai Early Warning Information Terhadap People Smuggling?. In this thesis, the author tries to describe how does Immigration Intelligence role in producing intelligence in the form of an early warning against people smuggling. How is the process of earnings warnings and how the implementation of the early warning. Beside that, the author tries to describe any efforts that can be undertaken by Immigration Intelligence in preventing the people smuggling. Technical writing this thesis uses descriptive qualitative method, so in doing the analysis the authors attempted to outline in writing where the data previously obtained from interviews and literature., This thesis entitled “Peran Pre-Emtif Intelijen Keimigrasian Sebagai Early Warning Information Terhadap People Smuggling”. In this thesis, the author tries to describe how does Immigration Intelligence role in producing intelligence in the form of an early warning against people smuggling. How is the process of earnings warnings and how the implementation of the early warning. Beside that, the author tries to describe any efforts that can be undertaken by Immigration Intelligence in preventing the people smuggling. Technical writing this thesis uses descriptive qualitative method, so in doing the analysis the authors attempted to outline in writing where the data previously obtained from interviews and literature.]"

"[ABSTRAKKrisis rupiah di tahun 1997-1999 adalah gejolak besar bagi ekonomi Indonesia dimana pertumbuhan ekonomi anjlok, sistem perbankan lumpuh, diikuti dengan konsekuensi sosial dan politik yang pahit. Diyakini jika otoritas moneter daan pembuat kebijakan lainnya dapat mengantisipasi krisis tersebut, segala konsekuensi yang tidak diinginkan tersebut dapat dikurangi dan mungkin dihindari.Sistem pendeteksian dini untuk krisis rupiah diharapkan dapat memberi waktu para pembuat kebijakan untuk mengantisipasi datangnya krisis. Namun salah satu faktor sukses yang kritikal dalam pembentukan sistem tersebut adalah seleksi indikator.Penelitian ini menerapkan Indication & Warning Intelligence (I&W)untuk sistem pendeteksian dini krisis rupiah dengan produk akhir berupa set indikator yang dianggap efektif memprediksi krisis rupiah. Dengan I&W diseleksi lima indikator, yang selanjutnya diuji keefektifannya dengan metode regresi logit, yang menyatakan bahwa terdapat tiga indikator yang dapat memprediksi krisis rupiah secara signifikan yang adalah: 1) Real Effective Exchange Rate, 2) Deposit Money Bank?s Foreign Asset Growth, dan 3) Inflasi.

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ABSTRACTCurrency crisis in Indonesia that took place in 1997-1999 was a major shock to the economy which plunge the growth, collapse the banking system with its bitter social and political consequences. It is acknowledged that if the monetary authority could anticipate such shock, the result would not be as devastating, it could be far lessen, and hopefully avoided.Early warning system for currency crisis is crucial for policy makers to anticipate the coming crisis with enough preparation. One of the most important factor in framing that system is indicators selection.This research apply Indication & Warning Intelligence (I&W) to early warning system of rupiah crisis with indicator set as its end product. With I&W five indicators are selected, which underwent further significance test with logistic regression method. This method results in three indicators being the most effective in predicting rupiah crisis, namely: 1) Real Effective Exchange Rate, 2) Deposit Money Bank?s Foreign Asset Growth, and 3) Inflation.;Currency crisis in Indonesia that took place in 1997-1999 was a major shock to the economy which plunge the growth, collapse the banking system with its bitter social and political consequences. It is acknowledged that if the monetary authority could anticipate such shock, the result would not be as devastating, it could be far lessen, and hopefully avoided.Early warning system for currency crisis is crucial for policy makers to anticipate the coming crisis with enough preparation. One of the most important factor in framing that system is indicators selection.This research apply Indication & Warning Intelligence (I&W) to early warning system of rupiah crisis with indicator set as its end product. With I&W five indicators are selected, which underwent further significance test with logistic regression method. This method results in three indicators being the most effective in predicting rupiah crisis, namely: 1) Real Effective Exchange Rate, 2) Deposit Money Bank’s Foreign Asset Growth, and 3) Inflation., Currency crisis in Indonesia that took place in 1997-1999 was a major shock to the economy which plunge the growth, collapse the banking system with its bitter social and political consequences. It is acknowledged that if the monetary authority could anticipate such shock, the result would not be as devastating, it could be far lessen, and hopefully avoided.Early warning system for currency crisis is crucial for policy makers to anticipate the coming crisis with enough preparation. One of the most important factor in framing that system is indicators selection.This research apply Indication & Warning Intelligence (I&W) to early warning system of rupiah crisis with indicator set as its end product. With I&W five indicators are selected, which underwent further significance test with logistic regression method. This method results in three indicators being the most effective in predicting rupiah crisis, namely: 1) Real Effective Exchange Rate, 2) Deposit Money Bank’s Foreign Asset Growth, and 3) Inflation.]"

"Wilayah pesisir adalah salah satu wilayah yang sangat rentan bencana. Salah satu bencana yang perlu mendapat perhatian serius di daerah pesisir termasuk di kota Jakarta adalah bencana yang disebabkan oleh banjir pantai. Permasalahan utama adalah saat ini belum tersedia sistem peringatan dini banjir pantai di Jakarta. Tujuan penelitian ini adalah membangun model sistem peringatan dini banjir pantai dengan memperhitungkan berbagai faktor dan menganalisis tingkat penerimaan masyarakat pada pengembangan sistem peringatan dini banjir pantai di daerah pesisir Jakarta. Metode yang digunakan adalah Mix Method (Kuantitatif dan Kualitatif). Pengembangan model dilakukan dengan memperhitungkan faktor meteorologi, klimatologi, oseanografi dan hidrologi dan juga melakukan analisis pada penerimaan masyarakat dengan deskripsi statistik, tabulasi silang dan analisis jalur. Hasil penelitian menunjukkan perbandingan tinggi muka laut pada saat kejadian banjir pantai antara model dan data observasi pada tanggal 29 Mei – 8 Juni 2016, 3 Januari – 13 Januari 2017 dan 28 November – 8 Desember 2017 menunjukkan korelasi yang baik yaitu nilai r masing - masing, 0,98, 0,99, dan 0,96. Hasil simulasi pada tanggal 5 Desember 2017 menunjukkan peta genangan dengan dampak terparah ada di wilayah Tanjung Priok, Marunda, Kalibaru dan Kamal Muara. Hasil analisis penerimaan masyarakat memperlihatkan adanya hubungan antara pengetahuan dan persepsi pada sikap masyarakat merespon model peringatan dini banjir pantai. Berdasarkan hasil penelitian dapat disimpulkan bahwa model yang dibangun dalam penelitian dapat digunakan sebagai sistem peringatan dini dalam mitigasi banjir pantai bagi masyarakat di pesisir Jakarta.

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The coastal area is one of the areas that are very vulnerable to disasters. One of the catastrophes that need serious attention in coastal areas, including in Jakarta, is a disaster caused by coastal flooding. The main problem is that currently, there is no coastal flood early warning system in Jakarta. This research aims to build a model of coastal flood early warning system by taking into account various factors and analyzing the level of public acceptance on the coastal flood early warning system in development in the coastal areas of Jakarta. The method used is the Mix Method (Quantitative and Qualitative). Model development is carried out by considering meteorological, climatological, oceanographic, and hydrological factors and conducting analysis on public acceptance with statistical descriptions, cross - tabulation, and path analysis. The results show the comparison of sea level between the model and the observation data at the time of coastal flooding on 29 May - 8 June 2016, 3 January - 13 January 2017 and 28 November - 8 December 2017 showed a good correlation, namely the respective r values, 0,98, 0,99, and 0,96. The simulation results on 5 December 2017 depict inundation maps with the worst impacts in the Tanjung Priok, Marunda, Kalibaru, and Kamal Muara areas. The public acceptance analysis results show that there is a relationship between knowledge and perceptions of people's attitudes in responding to the coastal flood early warning model. From the results of the study, it can be concluded that the model built in the study can be used as an early warning system in coastal flood mitigation for communities on the coast of Jakarta."

"Banjir di Jakarta merupakan masalah yang kompleks yang dipengaruhi oleh kombinasi faktor geografis, sosial, ekonomi, dan lingkungan. Studi ini berfokus pada prediksi banjir dengan membandingkan data stasiun darat Automatic Rain Gauge (ARG) dan data satelit Climate Hazards Group InfraRed Precipitation (CHIRPS) menggunakan Adaptive Neurofuzzy Inference System (ANFIS) yang terintegrasi dengan Principal Component Analysis (PCA). Dataset mencakup pengukuran curah hujan dari ARG dan CHIRPS, serta data ketinggian air dari tahun 2014 hingga 2020. ARG menyediakan data curah hujan lokal yang akurat, sementara CHIRPS menawarkan cakupan curah hujan regional yang luas. Teknik praproses seperti imputasi rata-rata, normalisasi data, dan metode interquartile range (IQR) digunakan untuk meningkatkan kualitas data. Model ANFIS-PCA, yang mengintegrasikan logika fuzzy dan pelatihan jaringan saraf tiruan, diterapkan dengan pembagian data 80:20 untuk pelatihan dan validasi. Ketika dilatih dengan data stasiun darat ARG dan pengukuran ketinggian air, model ANFIS-PCA menunjukkan akurasi yang superior, dengan root mean square error (RMSE) sebesar 0,13, mean absolute error (MAE) sebesar 0,12, dan R² sebesar 0,82. Sebaliknya, model ANFIS tanpa PCA menghasilkan kesalahan yang lebih tinggi, dengan RMSE 6,3, MAE 6,2, dan R² 0,74. Pelatihan dengan data satelit CHIRPS menghasilkan kesalahan yang jauh lebih tinggi (RMSE 30,14, MAE 24,05, R² 0,42). Sedangkan hasil ANFIS – PCA menghasilkan akurasi yang lebih bagus (RMSE 4,8, MAE 2,0 dan R² 0,55) . Hasil penelitian menunjukkan bahwa ANFIS-PCA memiliki kinerja yang lebih baik dibandingkan model ANFIS tanpa PCA, terutama ketika dilatih dengan data dari stasiun darat. Integrasi PCA berhasil mengurangi dimensi data, meningkatkan efisiensi komputasi dan akurasi model. Selain itu hasil ini juga menegaskan keunggulan pengukuran curah hujan data ground station untuk prediksi banjir, mempunyai angka presisi yang lebih tinggi dan kerentanan yang lebih rendah terhadap kesalahan dibandingkan data satelit. Sementara itu data satelit CHIRPS menawarkan cakupan spasial yang lebih luas.

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Flooding in Jakarta is a complex issue influenced by a combination of geographical, social, economic, and environmental factors. This study focuses on flood prediction by comparing ground station data from Automatic Rain Gauges (ARG) and satellite data from the Climate Hazards Group InfraRed Precipitation (CHIRPS) using the Adaptive Neuro-Fuzzy Inference System (ANFIS) integrated with Principal Component Analysis (PCA). The dataset includes rainfall measurements from ARG and CHIRPS, as well as water level data from 2014 to 2020. ARG provides accurate local rainfall data, while CHIRPS offers broad regional precipitation coverage. Preprocessing techniques such as mean imputation, data normalization, and the interquartile range (IQR) method were employed to enhance data quality.

The ANFIS-PCA model, which integrates fuzzy logic and neural network training, was implemented using an 80:20 data split for training and validation. When trained with ARG ground station data and water level measurements, the ANFIS-PCA model demonstrated superior accuracy, achieving a root mean square error (RMSE) of 0.13, mean absolute error (MAE) of 0.12, and R² of 0.82. In contrast, the ANFIS model without PCA yielded higher errors, with RMSE of 6.3, MAE of 6.2, and R² of 0.74. Training with CHIRPS satellite data resulted in significantly higher errors (RMSE 30.14, MAE 24.05, R² 0.42). Meanwhile, the ANFIS-PCA model trained on combined datasets showed improved performance, achieving RMSE of 4.8, MAE of 2.0, and R² of 0.55.

The results indicate that the ANFIS-PCA model outperforms the ANFIS model without PCA, particularly when trained with ground station data. The integration of PCA successfully reduced data dimensionality, improving computational efficiency and model accuracy. Furthermore, the findings reaffirm the superiority of ground-based measurements for flood prediction due to their higher precision and lower susceptibility to errors compared to satellite-derived data, while CHIRPS satellite data offers wider spatial coverage."